Description:FIELD OF THE INVENTION
[0001] The present invention relates to the field of industrial automation. More specifically, the present invention relates to logistics optimization. Even more specifically, the present invention relates to an AI-powered computer vision system that integrates with new or existing cameras, CCTV, smartphone, or portable imaging devices, achieving high accuracy in damage detection and OCR, supporting real-time analytics, and providing predictive maintenance without requiring new infrastructure.

BACKGROUND OF THE INVENTION
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Efficient container management is vital in logistics, but traditional inspection methods—relying on manual image capture and reporting—are slow, error-prone, and lack reliable audit trails. While some global solutions offer OCR and basic damage detection, they often require costly infrastructure, perform poorly with low-quality images, and fail to adapt to varied conditions in emerging markets like India. These systems lack comprehensive health records, real-time audit trails, predictive analytics, and flexibility for non-standard setups such as rail yards or rural hubs, making them unsuitable for mid-scale logistics operations.
[0004] Patent Number KR102348360B1 discloses a container scanning system integrated with automated port logistics to scan multiple containers in parallel at high-throughput ports, minimizing trade disruption. While effective for threat detection and secure data handling, it is limited in scope. It lacks detailed container health assessments, predictive analytics, and real-time reporting. Its reliance on fixed infrastructure makes it unsuitable for mid-scale operators, rural hubs, and rail yards. The system does not handle poor image conditions well and offers no audit trails or adaptability to diverse environments. These limitations like manual processes, incomplete records, high costs, and lack of scalability, are not addressed by the patent.
[0005] There is a pressing need for solutions that can seamlessly integrate with existing systems, offer high accuracy in diverse conditions, and provide real-time insights to support decision-making and predictive maintenance, while also reducing high inspection costs and delays affecting yard throughput. The ability to automate the inspection process using AI-powered systems can significantly reduce the time and cost associated with manual inspections, while also providing more reliable
OBJECTIVES OF THE INVENTION
[0006] The principal objective of the present invention is to overcome the disadvantages of the prior art.
[0007] An objective of the present invention is to automate the inspection of shipping containers by utilizing AI-powered image analysis, thereby minimizing the need for manual inspection and enabling repair cost estimation.
[0008] Another objective of the present invention is to leverage existing cameras and surveillance infrastructure for capturing container images, reducing the requirement for additional hardware deployment, while supporting operability across standard CCTV.
[0009] Another objective of the present invention is to develop a system capable of robust Optical Character Recognition (OCR) and object detection under difficult environment conditions such as motion blur, poor lighting, and physical damage to text or surface.
[0010] Another objective of the present invention is to enable real-time analysis and decision-making through edge AI processing, minimizing latency and allowing instant detection of container damage, object counts, and health discrepancies.
[0011] Another objective of the present invention is to provide an easy to use interface for generation of automated inspection reports, thereby streamlining documentation and ensuring consistency in inspection outcomes.
[0012] Another objective of the present invention is to use advanced AI techniques for highly accurate damage detection, object counting, and tracking across angles and complex scenarios.
[0013] Another objective of the present invention is to implement multi-angle image processing that enables thorough inspection and damage assessment from all perspectives (top, bottom, front, rear, and sides) of containers or objects.
[0014] Another objective of the present invention is to maintain detection accuracy and system reliability in adverse weather conditions (rain, fog, snow) and during night-time operations.
[0015] Another objective of the present invention is to offer an intuitive user interface that supports ease of use for non-technical users, encouraging quick adoption across logistics, warehousing, and industrial sectors.
[0016] Another objective of the present invention is to enable client specific customization and continuous learning based on operational data, improving model accuracy over time and adapting to evolving real-world scenarios.
[0017] Another objective of the present invention is to assess the integrity and status of container security seals to enhance cargo safety and prevent unauthorized access.
[0018] Another objective of the present invention is to facilitate predictive maintenance by identifying wear patterns and damage trends in containers over time.
[0019] Yet another objective of the present invention is to provide an efficient, scalable, and cost effective solution for container inspection, suitable for deployment in high-throughput logistics environments.
[0020] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION
[0021] The present invention relates to an AI-based computer vision system that integrates AI and OCR technologies for automated container inspection, damage segmentation, repair cost estimation, and health assessment, security verification, and document digitization, with improved accuracy, by leveraging existing imaging device infrastructure in logistics and supply chain operations.
[0022] According to an embodiment of present invention, the system for automated container inspection may include a training module that can be trained on diverse container images under various lightening conditions, camera angle and container orientation.
[0023] According to another embodiment of present invention, the system comprises an image acquisition subsystem may comprise new or existing closed-circuit television cameras configured to capture container images.
[0024] According to another embodiment, the system comprises a deep-learning-based image enhancement module for preprocessing the captured container images using techniques such as noise reduction, image enhancement, or image normalization.
[0025] According to another embodiment, the system comprises an AI-powered damage detection module can analyze the container images for multiple types of container damage and security seal status using a Convolutional Neural Network technique.
[0026] According to another embodiment, the system comprises an OCR module may extract container-related codes from the container images.
[0027] According to another embodiment, the system comprises a reporting and analytics processing system can generate inspection reports and provide analytics based on the detected container damage types, security seal status, and extracted container-related codes.
[0028] According to another embodiment, the computer-implemented method for automated container inspection may involve training an AI-powered damage detection module and an OCR module on diverse container images under various conditions. Container images may be captured using an image acquisition subsystem comprising new or existing closed-circuit television cameras. The preprocessing the captured container images may be done in the deep-learning based image enhancement module using techniques such as noise reduction, image enhancement, or image normalization.The AI-powered damage detection module can analyze the container images to detect multiple types of container damage and security seal status, utilizing a Convolutional Neural Network technique. The OCR module may extract container-related codes from the container images. Inspection reports and analytics can be generated based on the detected container damage types, security seal status, and extracted container-related codes using a reporting and analytics processing system.
[0029] According to another embodiment, the OCR module may utilize a proprietary OCR framework to extract container numbers, ISO codes, and vehicle IDs in both vertical and horizontal orientations including Bill of Landing (BoL) and invoice scanning and maintain digital records.
[0030] According to another embodiment, the reporting and analytics processing system may be configured to generate actionable insights based on the inspection results.
[0031] According to another embodiment, AI-powered damage detection module comprises a precise segmentation submodule configured to perform pixel-level segmentation of containers and defects.
[0032] According to another embodiment, the system may comprises a vision-based container lifecycle management module that configured to monitor and manage the container’s lifecycle stage, continuously track and document the containe’s health, cargo condition, associated stakeholders throughout each stage and provide end-to-end visibility, accountability and operational efficiency through vision-based data capture and automated record keeping.
[0033] According to yet another embodiment, the system may be camera-agnostic, it may be operable with a variety of imaging sources including but not limited to fixed CCTV cameras, smartphone cameras, mobile camera rigs, and low-cost imaging devices. This hardware flexibility enables the system to be deployed across diverse logistics environments without dependence on specialized camera arrays or proprietary equipment
[0034] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0036] In the figures, similar components and/or features may have the same reference label. Further various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any of the similar components having the same reference label irrespective of the second reference label.
[0037] FIG. 1 illustrates a block diagram of an automated container inspection system, according to an embodiment of the present invention.
[0038] FIG. 2 shows a block diagram of a reporting and analytics processing system for container inspection, according to an embodiment of the present invention.
[0039] FIG. 3 shows a flow chart of a method for automated container inspection, according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION
[0040] In this document, singular articles like "a," "an," and "the" can also imply plural references unless the context clearly indicates otherwise. Similarly, "in" can include "in" and "on." If a component or feature is described with "may," "can," "could," or "might," it means that component or feature isn't a mandatory inclusion or characteristic. The provided exemplary embodiments are for thoroughness and to fully convey the disclosure's scope; they aren't exhaustive. All stated embodiments and specific examples cover both existing and future structural and functional equivalents.
[0041] Numerical parameters in the descriptions and claims are approximations that can vary based on desired properties. These should be interpreted considering the number of significant digits and standard rounding. While broad numerical ranges are approximate, specific examples report values as precisely as possible, acknowledging inherent errors from standard deviation in testing measurements.
[0042] The present invention relates to an AI-based computer vision system that integrates AI and OCR technologies for automated container inspection and health assessment, security verification, and document digitization, at high accuracy, by leveraging new or existing camera infrastructureincluding but not limited to CCTV, smartphone or portable imaging device, in logistics and supply chain operations.
[0043] According to an embodiment of present invention, the system (100) for automated container inspection may include a training module (102), an image acquisition subsystem (104), an AI-powered damage detection module (106), an Optical Character Recognition (OCR) module (108), and a reporting and analytics processing system (110). The training module (102) can be trained on diverse container images under various conditions to achieve accuracy in damage detection and OCR. The image acquisition subsystem (104) may integrate with existing CCTV systems to capture container images, eliminating the need for additional hardware. The system (100) may also include a deep-learning-based image enhancement module (112) for preprocessing captured images, a maintenance predictor for real-time health monitoring, and an automated documentation processing system for generating container inspection documentation.
[0044] According to another embodiment, the AI-powered damage detection module (106) can analyze container images for various damage types and security seal status using Convolutional Neural Network techniques, automating security seal and label verification. The OCR module (108) may extract container-related codes, such as container numbers, ISO codes, and vehicle IDs, ensuring accuracy even in challenging conditions. The reporting and analytics processing system (110) can generate inspection reports and provide analytics based on detected container damage types, security seal status, and extracted codes, facilitating automated container health assessment and logistics optimization.
[0045] According to another embodiment, the OCR module is further configured to automate the scanning of Bills of Lading (BoL) and invoices, maintaining digital records of the scanned documents. This functionality ensures that critical logistics documents are digitized and stored efficiently, allowing for easy retrieval and management. By automating the scanning process, the system reduces manual effort and minimizes the risk of errors, ensuring that all documents are accurately captured and maintained in a digital format.
[0046] Referring to FIG. 1, a block diagram of a system (100) designed for automated container inspection, showcasing its various components and their interactions is illustrated. The system (100) is structured to enhance the accuracy and efficiency of container inspections by leveraging advanced AI technologies. At the core of the system (100) is the training module (102), which is responsible for training on a diverse set of container images captured under various conditions. This module (102) ensures that the system (100) achieves accuracy in both damage detection and optical character recognition (OCR) across different scenarios.
[0047] According to another embodiment, the image acquisition subsystem (104) is a critical component of the automated container inspection system (100), designed to capture comprehensive visual data necessary for accurate analysis and reporting. This subsystem (104) strategically utilizes new/existing closed-circuit television (CCTV) cameras, smartphone, or portable imaging device which are placed at key locations such as container entry and exit gates or within container yards. By leveraging these existing infrastructures, the system (100) effectively eliminates the need for additional hardware investments or significant infrastructure modifications.
[0048] According to another embodiment, the image acquisition subsystem (104) may designed to be hardware-neutral, supporting integration with a variety of camera types such as IP-based CCTV systems, smartphone cameras, or portable. The system architecture may allows it to operate effectively across different resolutions, frame rates, and image qualities, ensuring adaptability in both urban yards and remote or low-infrastructure areas.
[0049] According to another embodiment, the cameras are configured to capture images from multiple angles, including but not limited to the front, rear, left side, right side, top, and bottom of the containers. This multi-angle approach ensures that all potential areas of damage or irregularities are documented, providing a holistic view of each container's condition. The captured images are then processed through advanced algorithms to enhance their quality, ensuring that subsequent analysis by AI-powered modules is based on optimal visual data.
[0050] According to another embodiment, the deep-learning-based image enhancement module (112) is an integral part of the automated container inspection system (100), designed to optimize the quality of images captured by the image acquisition subsystem (104). This module (112) employs advanced deep learning techniques to perform a series of image processing tasks, including noise reduction, image enhancement, and normalization. These processes are crucial for improving the clarity and detail of images, particularly those captured under challenging conditions such as low light, motion blur, or adverse weather.
[0051] According to another embodiment, by enhancing the quality of the images, the module (112) ensures that the AI-powered damage detection module (106) and Optical Character Recognition (OCR) modules (108) receive the best possible input for analysis, thereby increasing the accuracy and reliability of the inspection results. The module (112) may optimized to run efficiently on low-power devices and existing surveillance infrastructure, minimizing the need for costly hardware upgrades or cloud-based processing.
[0052] According to another embodiment, the AI-powered damage detection module (106) may be a sophisticated component of the automated container inspection system (100), leveraging custom-trained Convolutional Neural Network (CNN) models to deliver precise and comprehensive damage analysis. This module (106) is adept at identifying and classifying up to 14 distinct types of container damage, including dents, rust, cracks, corner post cuts, push-in, push-out, and various rust locations such as back side, front side, and upper part rust. By utilizing advanced CNN techniques, the module (106) not only detects the presence of these damages but also assesses their severity based on size and location. This detailed classification aids in making informed repair decisions, ensuring that maintenance efforts are both effective and efficient. Its integration into the broader system allows for seamless data flow and real-time analysis, contributing to the overall goal of automated, accurate, and efficient container inspection.
[0053] By analyzing high-resolution images captured by the integrated cameras, the CNN model is trained to detect and classify various conditions of security seals, such as intact, tampered, incorrect or missing. The model processes visual inputs to recognize subtle differences in seal appearance, leveraging its ability to learn complex patterns and features. This automated verification process enhances security by ensuring that only containers with compliant seals proceed through logistics operations, thereby preventing unauthorized access and ensuring regulatory compliance.
[0054] According to another embodiment, the AI-powered damage detection module (106) further comprises a precise segmentation submodule that performs pixel-level segmentation of both containers and detected damages. This advanced segmentation provides a significant enhancement over traditional bounding-box methods by accurately isolating container boundaries within multi-container image frames, enabling reliable container-specific tracking across multiple events, such as yard entry and exit. Furthermore, this submodule delineates defects based on their actual contours, including irregular or diagonal shapes, rather than approximating with rectangles. The segmented regions then converted into real-world dimensions—such as length, width, depth, and surface area—via a proprietary dimensional conversion algorithm calibrated to the yard’s imaging setup. These measurements may support precise cost estimation and repair planning, thereby transforming visual inspection into actionable maintenance intelligence.
[0055] According to another embodiment, the Optical Character Recognition (OCR) module (108) may be a pivotal element of the automated container inspection system (100), specifically designed to accurately extract and interpret critical container-related information. This module (108) employs specialized OCR models that have been meticulously trained on fonts commonly used for container numbers, ISO codes, and vehicle numbers/IDs, ensuring high precision in diverse operational environments. It is capable of reading both horizontal and vertical container IDs, as well as ISO codes, while being adaptive to angle corrections to read vertically aligned, skewed or distorted text, which are essential for verifying container identity and compliance with international standards.
[0056] According to another embodiment, the OCR module (108) is equipped with robust error-handling capabilities, allowing it to effectively manage challenges posed by blurred, partially occluded, or distorted characters. This ensures reliable data extraction even under suboptimal conditions, such as low-resolution images or adverse weather impacts. By integrating advanced image processing techniques, the module (108) enhances the accuracy of character recognition, facilitating seamless data flow into the system's analytics and reporting components.
[0057] According to another embodiment, the processed data from these modules is then fed into the reporting and analytics processing system (110), which generates detailed inspection reports and provides analytics based on the detected damage types, security seal status, and extracted container codes. This system (110) is designed to offer actionable insights, enhancing decision-making processes in logistics operations. The integration of these components allows for seamless operation, ensuring that the system can perform real-time monitoring and reporting, thereby optimizing container health assessment and logistics management.
[0058] According to another embodiment, , the system (100) further comprises a vision-based container lifecycle management module (120), which enables continuous tracking and documentation of each container’s lifecycle stages, including but not limited to, yard entry, destuffing, visual surveying, repair inspection, restuffing, and final yard exit. This module (120) leverages vision data captured at key checkpoints to automatically record container condition, cargo health, and the involved operational stakeholders. By automating these steps, the system (100) ensures end-to-end visibility and accountability, reducing manual paperwork and facilitating transparent audit trails. This lifecycle management framework significantly enhances operational planning, maintenance scheduling, and SLA compliance across logistics hubs.
[0059] FIG. 2 illustrates a block diagram of a Reporting and Analytics Processing System (110) designed for automated container inspection. This system (110) is integral to the automated container inspection process, providing data analysis and reporting capabilities. The system comprises several key components, including a Maintenance Predictor (116), an Automated Documentation Processing System (118), and an Analytics Dashboard (114). These components are interconnected to facilitate container inspection and maintenance processes.
[0060] According to another embodiment, the reporting and analytics processing system (110) may designed to maintain tamper-proof, audit-grade logs, which are accessible through an analytics dashboard. This feature ensures that all data related to container inspections is securely recorded and easily retrievable, providing a reliable audit trail. The logs may protected against unauthorized modifications, thereby preserving data integrity and enhancing transparency in logistics operations. Users may access these logs via the analytics dashboard, which offers a user-friendly interface for monitoring and reviewing inspection data, facilitating informed decision-making and compliance verification.
[0061] According to another embodiment, the Maintenance Predictor (116) is a component that forecasts container health and maintenance needs. It may utilize container’s historical data and predictive algorithms to assess the condition of containers, enabling proactive maintenance and reducing downtime. This real-time predictive capability ensures that containers are kept in condition, enhancing the reliability and efficiency of logistics operations.
[0062] According to another embodiment, the Automated Documentation Processing System (118) is responsible for handling and digitizing documents related to container inspections. It may employ Optical Character Recognition (OCR) technology to convert physical documents, such as container numbers, ISO codes, vehicle numbers, bills of Landing (BoL) and compliance forms, into digital formats. This digitalization functionality streamlines the digitization and storage of essential logistics documents, enabling quick retrieval and efficient management. By automating the scanning process, it reduces manual effort, minimizes errors, and ensures accurate and reliable digital documentation.
[0063] According to another embodiment, the Analytics Dashboard (114) provides an interface for accessing data and insights generated by the system (116 & 118). It may display real-time analytics, inspection reports, and maintenance forecasts, allowing users to make decisions based on the latest data. The analytics dashboard (114) can be customized to meet specific user needs, offering a view of container health and inspection results.
[0064] In an inference, these components work together to ensure container inspection and maintenance processes. The Maintenance Predictor (116) feeds data into the Automated Documentation Processing System (118), which in turn updates the Analytics Dashboard (114) with the latest information. This integration allows for data flow and real-time updates, enhancing the functionality of the system. By automating processes and providing insights, the Reporting and Analytics Processing System (110) supports the goals of the system, including damage detection, security verification, and document management.
[0065] Referring to FIG. 3, a flowchart detailing a computer-implemented method for automated container inspection. The process begins with the training of an AI-powered damage detection module (104) and an Optical Character Recognition (OCR) module (106). This training is crucial for achieving accuracy in detecting various container damage types and extracting container-related codes under diverse conditions. The training module (102) is designed to handle images captured under different lighting conditions, camera angles, and container orientations, ensuring performance across various scenarios.
ARCHITECTURAL OVERVIEW
[0066] According to another embodiment, the backbone of the module (102) incorporates a Residual Efficient Layer Aggregation design (R-ELAN), which is instrumental in enhancing feature reuse and learning stability. This design employs Layer Aggregation, creating multiple feature flow paths that enable the combination of shallow and deep features across the network. This approach is beneficial for retaining both fine texture details and higher-level semantic context, which are essential for accurate analysis.
[0067] According to additional embodiment, Residual Scaling may be implemented, integrating special residual paths at the block level to improve gradient flow and convergence during training. These enhancements are particularly beneficial for learning complex structures such as container corners, damaged edges, seal stamps, and rust trails, ensuring that the system can effectively identify and assess these intricate features.
[0068] According to another embodiment, the model may incorporate a specialized Area Attention mechanism that enhances its ability to process images efficiently and accurately. This mechanism works by dividing the image into sub-regions, allowing for localized self-attention within each segment. Through Region Splitting, the feature maps are divided into N×N regions, which significantly reduces memory and computation costs. Within each of these regions, Self-Attention is applied to capture long-range dependencies while maintaining spatial coherence. This approach is particularly advantageous for detecting long container numbers, seam damage, or occluded plates, as it ensures that important spatial relationships are preserved. Notably, the model does not require positional encoding; instead, it utilizes a lightweight 7×7 depthwise convolution to provide the necessary spatial structure, thereby reducing computational overhead and enhancing processing efficiency.
[0069] According to another embodiment, Flash Attention Optimization may be employed within the model to enhance both training and inference efficiency. By integrating Flash Attention into the attention layers, the model achieves memory-efficient computation and faster throughput. This optimization is particularly beneficial as it allows for higher batch sizes and quicker convergence during the training process, all without compromising accuracy. Such improvements are crucial when working with large datasets that feature varied container orientations, as they ensure that the model can handle extensive data while maintaining performance standards. The use of FlashAttention thus contributes to the model's overall effectiveness and efficiency in processing complex and diverse image data.
[0070] According to another embodiment, the network's neck component may be designed as a Feature Pyramid Network (FPN) that is further enhanced with path aggregation and attention-driven fusion, creating a robust framework for multi-scale feature fusion. The FPN layers play a critical role by merging low-resolution semantic features with high-resolution detail layers, which allows the network to maintain a comprehensive understanding of the image across different scales. This architecture significantly improves the model's ability to detect objects of varying sizes simultaneously. It is capable of identifying large objects such as entire containers and trucks, medium-sized items like hazard symbols and ISO labels, and small targets including bolts, seal barcodes, and cracks. By effectively integrating features from multiple scales, the neck component ensures that the network can accurately and efficiently process complex images, enhancing its overall detection capabilities.
CONFIGURATION PARAMETERS:
[0071] According to another embodiment, the detection head of the model is designed to be flexible, operating as either anchor-based or anchor-free depending on the training configuration. This adaptability allows the model to optimize its performance based on specific requirements. The detection head includes several key components that contribute to its functionality. Bounding Box Prediction is achieved through regression layers that determine the dimensions of the box, specifically the coordinates (x, y) and size (w, h). This precise localization is crucial for accurately identifying objects within the image.
[0072] According to additional embodiment, the Objectness Confidence Score is calculated to indicate the likelihood of an object's presence within the predicted bounding box, providing a measure of certainty for each detection. Class Prediction is performed using softmax or sigmoid functions, which classify the detected objects into specific categories such as Container, Container ID, Seal, License Plate, Hazard Symbol, and Damage. This comprehensive classification system ensures that the model can effectively distinguish between various object types, enhancing its utility in diverse applications.

Table I: Training Parameters
Parameter Value / Strategy
Input Resolution 1280×1280 pixels
Batch Size 16–32 (depending on GPU memory)
Learning Rate 0.001 with cosine decay scheduler
Optimizer AdamW or SGD with momentum
Loss Functions GIoU loss for boxes, Focal loss for class
Data Augmentation Mosaic, MixUp, color jitter, affine transform
IoU Threshold Tuned per task: 0.5 for object retention, 0.3–0.4 for damage detection
Epochs 1000–1500, depending on convergence

[0073] According to exemplary embodiment, the model's training configuration is meticulously designed to optimize performance across various tasks, as represented in Table I. It utilizes an input resolution around 1280×1280 pixels, ensuring high-quality image processing. The batch size is adjustable, ranging from 16 to 32, depending on the available GPU memory, allowing for flexibility in handling different computational resources. A learning rate of 0.001 is employed, with a cosine decay scheduler to gradually reduce the rate as training progresses, enhancing convergence. The optimizer used is either AdamW or SGD with momentum, both of which are effective in managing the model's learning dynamics. For loss functions, the model applies Generalized Intersection over Union (GIoU) loss for bounding box predictions and Focal loss for class predictions, ensuring precise localization and classification. Data augmentation techniques such as Mosaic, MixUp, color jitter, and affine transform are incorporated to increase the model's robustness to variations in the input data. The Intersection over Union (IoU) threshold is finely tuned per task, set at 0.5 for object retention and between 0.3 to 0.4 for damage detection, balancing precision and recall. The training process spans 1000 to 1500 epochs, depending on the convergence rate, ensuring thorough learning and model stability.
[0074] According to another embodiment, the next step involves capturing container images using an image acquisition subsystem (104), which may include new or existing closed-circuit television cameras. The system may integrate seamlessly with existing CCTV camera setups, potentially eliminating the need for additional hardware or major infrastructure changes. The captured images may be processed through Application Programming Interfaces (APIs) to the Visotonics Cloud, where container damage detection reports and container ID/ISO information can be generated.
[0075] According to another embodiment, the image preprocessing stage employs a sophisticated deep-learning-based enhancement network designed to ensure robust detection and recognition in real-world, low-quality scenarios, such as those involving varying lighting conditions, rain, or blur. This enhancement module (112) is applied prior to any detection processes, setting the foundation for accurate analysis.
[0076] According to another embodiment, key components and functionalities of this module (112) include Multi-scale Input Decomposition, where the image is processed through multiple down-sampled streams. This approach extracts both fine-grained and global features, enabling the capture of text edges for Optical Character Recognition (OCR) and large-scale patterns indicative of damage or seal presence. Additionally, Hierarchical Feature Fusion is another critical component, where features from all scales are combined using attention blocks. These blocks learn to weigh the importance of each resolution, ensuring detailed spatial recovery, which is crucial for character recognition and crack detection.
[0077] According to another embodiment, Spatial Attention Mechanisms are employed to enhance the network's focus on significant features such as cracks, seams, or serial numbers. By utilizing channel and spatial attention, the network significantly improves OCR accuracy, particularly on metallic surfaces and low-contrast labels. The module also incorporates Residual and Dense Connections, which help preserve fine visual structures during denoising or enhancement. This is especially effective in areas with both texture, like rust, and characters, such as ISO codes or license plates.
[0078] According to another embodiment, the preprocessing stage utilizes a combination of loss functions to optimize image enhancement. Charbonnier Loss is used for smooth restoration, Perceptual Loss (derived from VGG/ResNet features) retains textural fidelity, and Edge-aware Loss preserves character contours for OCR. These loss functions collectively ensure that the enhanced images exhibit improved visual clarity, color consistency, edge sharpness, and structural consistency. These enhancements are critical for text and number clarity and are particularly important for crack detection in container damage assessment, ultimately contributing to the overall accuracy and reliability of the automated inspection process.
[0079] According to another embodiment, the captured images are then processed by the AI-powered damage detection module (106), which utilizes Convolutional Neural Network techniques to identify a range of container damage types, including dents, cracks, and rust, as well as to assess the status of security seals. This module is capable of detecting at least 14 different damage types, ensuring comprehensive damage assessment.
[0080] According to another embodiment, the AI-powered damage detection module (106) leverages a Convolutional Neural Network (CNN) to perform a comprehensive detection flow, ensuring precise identification and analysis of container conditions. The process begins with Feature Extraction, where convolutional layers meticulously extract texture, shape, and contextual information from the input images. This foundational step is crucial for understanding the intricate details present in the visual data. Following this, Pyramid Fusion is employed to combine deep and shallow features, enabling the detection of objects at varying scales, such as fine cracks and large plate numbers. This multi-scale approach ensures that the model can accurately identify both minute and prominent features within the images. The module (106) then proceeds to Bounding Box Prediction, where each feature map cell predicts the coordinates (x, y), size (w, h), confidence of object presence, and class scores for multiple predefined classes. This step is essential for localizing and classifying detected objects. Post-processing involves the application of Non-Maximum Suppression to eliminate overlapping boxes, retaining only those with the highest confidence for the final output. This ensures that the most accurate detections are prioritized. The module's Real-time Inference capability, achieving over 60 frames per second (FPS) on mid-tier GPUs, allows for live deployment in dynamic environments such as ports or container yards, facilitating immediate and efficient container inspection.
[0081] According to another embodiment, the OCR module (108) extracts container-related codes from the images. This module (108) employs a proprietary OCR framework to accurately extract container numbers, ISO codes, and vehicle IDs, even from low-resolution or partially obstructed views. The OCR module's ability to handle text in both vertical and horizontal orientations with accuracy is critical for reliable container identification and verification against shipment records. Additionally, the OCR module is designed to automatically scan Bills of Lading (BoL) and invoices, creating and maintaining accurate digital records. This ensures efficient digitization and storage of key logistics documents, enabling easy access and management while reducing manual effort and minimizing errors through automation.
[0082] According to another embodiment, the OCR module (108) is an advanced, end-to-end system that seamlessly integrates detection and recognition into a unified model specifically optimized for scene text. Its architecture is designed to efficiently handle the complexities of text detection and recognition in diverse environments. The module (108) employs a feature-based heatmap regression approach for Text Detection, marking the centerlines and contours of text blocks without relying on box proposals or heuristics. This method streamlines the detection process and enhances accuracy. The Sequence Encoder utilizes a transformer-style encoder to capture long-range dependencies between characters and visual patterns, effectively managing variable-length texts such as ISO codes and number plates.
[0083] According to additional embodiment, the Recognition Decoder predicts character sequences using a multi-head attention mechanism, with positional encoding providing spatial awareness. An optional Guided Enhancement feature allows for manual attention hinting, improving accuracy in cluttered scenes. Post-processing involves lexicon-based error correction, applying ISO code validation and country-specific plate rules to auto-correct minor OCR misreads, and confidence thresholding to ensure only results above a certain confidence score are retained. The module's task-specific OCR workflow includes container identification, seal verification, truck number plate reading, and hazard symbol detection, each tailored to ensure precise and reliable text recognition and verification in various logistical contexts.
[0084] Finally, the system (100) generates inspection reports and provides analytics based on the detected damage types, security seal status, and extracted codes. The reporting and analytics processing system (110) plays a role in transforming raw data into insights, facilitating logistics optimization and ensuring regulatory compliance. This system may also support real-time analytics and reporting through edge processing capabilities, reducing processing time and operational load on central systems. The flowchart in FIG. 3 effectively encapsulates the method, detailing the sequential steps involved in the automated container inspection process, from training the module to report generation.
[0085] According to another embodiment, the system (100) may designed to be camera-agnostic and multi-source operable, allowing it to perform real-time container inspection using a variety of imaging sources. These include but not limited to static CCTV setups, mobile surveillance cameras, smartphones, and low-cost imaging devices, without requiring proprietary arrays. The system may capable of functioning in automated mode even using only a smartphone, with secure activity validation mechanisms to prevent tampering. It supports multi-source image capture and processing within 20 seconds using minimal computational resources, whether deployed at the edge (on-premises) or in the cloud, depending on available connectivity. In offline scenarios, data may be locally stored and synchronized with cloud services upon reconnection, ensuring continuous operation even in connectivity-deficient remote yards. The system may be deployed via compact, low-power edge AI modules, which may operate within solar-powered enclosures, providing uninterrupted functionality. Inspection results may displayed on a live dashboard and exported via automated PDF reports, enabling real-time decision-making and streamlined documentation.
[0086] According to exemplary embodiment, the system (100) for automated container inspection is designed to accommodate a wide range of operational parameters, as shown in Table II, ensuring flexibility and adaptability in various environments. It supports container lengths of 10, 20, and 40 feet, with widths ranging from 7.5 feet (2286 mm) to 8 feet (2438 mm), and heights of 7.5 feet (2286 mm), 8.5 feet (2591 mm), and 9.5 feet. The system (100) is capable of detecting over 14 types of defects, including dents, rust, cracks, and cuts, with defect lengths ranging from 1 mm to the full container dimensions. Depending on the lane size and application, the system can utilize between 1 to 6 cameras per lane, with image resolutions from 1 MP to 13 MP, and a CCTV frame rate ranging from 10 fps to 60 fps. The camera field of view can extend up to 120°, allowing comprehensive coverage. Report generation is efficient, taking between 1 to 30 seconds, and the system (100) can handle container movement speeds from stationary to 30 kmph, making it suitable for both static and moving vehicle inspections.
TABLE II: Range of operational parameters
Parameter Workable Range / Value
Container Length 10ft, 20ft, 40ft
Container width 7.7 ft (2286mm) to 8 ft (2438mm)
Container height 7.5 ft (2286 mm), 8.5 ft (2591 mm), 9.5 ft (2896 mm)
Defect types detected 14+ types (dents, rust, cracks, cuts, bulges, etc.)
Defect length range 1 mm to full container length/width (up to 40 ft)
No. Of cameras per lane 1 to 6 (depending on lane size & application)
Image resolution 1 MP to 13 MP (based on yard setup & lighting)
CCTV Frame rate 10 fps to 60 fps
Camera field of view Up to 120°
Report generation time 1 second to 30 seconds
Container movement speed 0 kmph (static) to 30 kmph (moving vehicles)

EXPERIMENTAL RESULTS:
[0087] According to exemplary embodiment, Table III illustrates the performance of the system (100) against prior work. The proposed system (100) achieves high precision, recall, and mean average precision (mAP) across various tasks. For container number detection, the system achieves a precision range of 98.8%, recall of 99.2%, and mAP@0.5 of 98.9%, with an OCR accuracy of 98.26% and a frame rate of 61 FPS. Seal detection and reading show a precision range of 98.5%, recall of 98.8%, and mAP@0.5 of 98.4%, with an OCR accuracy of 98.7% and a frame rate of 59 FPS. Number plate reading achieves a precision range of 98.7%, recall of 98.9%, and mAP@0.5 of 98.5%, with an OCR accuracy of 98.5% and a frame rate of 62 FPS. Hazard symbol detection and container damage detection show precision ranges of 98.3% and 98.2%, respectively, with recall ranges of 98.8% and 98.1%, and mAP@0.5 ranges of 98.5% and 98.7%, both with a frame rate of 60 FPS.
Table III: Performance of the present disclosure against prior art
Task Method Precision Recall mAP@0.5 OCR Acc. FPS
Container Number Detection Proposed + OCR 98.8% 99.2% 98.9% 98.6% 61
Seal Detection + Reading Proposed + OCR 98.5% 98.8% 98.4% 98.7% 59
Number Plate Reading Proposed + OCR 98.7% 98.9% 98.5% 98.5% 62
Hazard Symbol Detection Proposed Detection 98.3% 98.8% 98.5% – 60
Container Damage Detection Proposed Detection 98.2% 98.1% 98.7% – 60

[0088] According to exemplary embodiment, the proposed system (100) demonstrates significant improvements over previous architectures as shown in Table IV, in terms of detection accuracy, OCR accuracy, and processing speed. Compared to the SSD + Tesseract method, which achieves a mean average precision (mAP) of 88.2%, OCR accuracy of 85.2%, and a speed of 18 frames per second (FPS), the present system shows a marked enhancement with a mAP of 98.5%, OCR accuracy of 98.7%, and a speed exceeding 60 FPS. Similarly, the YOLOv8 + Tesseract method, with a mAP of 89.6%, OCR accuracy of 88.1%, and a speed of 23 FPS, and the Faster R-CNN + Custom OCR method, with a mAP of 89.9%, OCR accuracy of 86.7%, and a speed of 12 FPS, both fall short of the proposed system's performance. These comparisons highlight the proposed system's superior capability in delivering high accuracy and efficiency, making it a robust solution for automated container inspection tasks.
Table IV: Comparision of present disclosure w.r.t. previous achitectures
Method mAP (Detection) OCR Accuracy Speed (FPS)
SSD + Tesseract 88.2% 85.2% 18
YOLOv8 + Tesseract 89.6% 88.1% 23
Faster R-CNN + Custom OCR 89.9% 86.7% 12
Proposed System 98.5% 98.7% 60+

[0089] This disclosure emphasizes that the described embodiments are illustrative and not restrictive. Numerous modifications and adaptations are possible within the inventive concepts, and the scope of the claims should be interpreted broadly. Specifically, "includes" and "including" are non-exclusive, and "at least one of" a group means only one element is required. The general nature of the disclosed embodiments allows others to modify and adapt them for various applications without departing from the core concept, and such variations are intended to be covered within the meaning and range of equivalents.
, C , Claims:We Claim:
1) A system (100) for automated container inspection, comprising:
A training module (102) trained on diverse container images under various conditions to achieve high accuracy in damage detection and optical character recognition (OCR) across the diverse container images and conditions, wherein the training module is configured by:
initializing a model architecture without utilizing pretrained weights;
training the model from scratch using the collected data and one or more advanced optimization techniques;
configuring the model architecture based on one or more task-specific constraints associated with container analysis;
wherein the model is trained in a manner that avaoids biases otherwise introduced by transfer learning from unrelated domains, thgereby improving accuracy and performance in the context of shipping container analysis;
an image acquisition subsystem (104) comprising new/existing closed-circuit television cameras configured to capture container images;
a deep-learning-based image enhancement module (112) for real-time preprocessing of the captured container images through at least one of noise reduction, image enhancement, or image normalization techniques;
an AI-powered damage detection module (106) configured to analyze the pre-processed container images for a plurality of container damage types and security seal status using Convolutional Neural Network technique;
an Optical Character Recognition (OCR) module (108) configured to extract container related codes from the container images; and
a reporting and analytics processing system (110) configured to generate inspection reports and provide analytics based on the detected container damage types, security seal status, and extracted container related codes.
2) A computer-implemented method for automated container inspection, comprising:
training an AI-powered damage detection module (106) and an Optical Character Recognition (OCR) module (108) on diverse container images under various conditions to achieve high accuracy in damage detection and OCR across the diverse container images and conditions;
capturing container images using an image acquisition subsystem (104) comprising new/existing closed-circuit television cameras;
preprocessing the captured container images via a deep-learning-based image enhancement module (112) through at least one of noise reduction, image enhancement, or image normalization techniques;
analyzing the preprocessed container images using the AI-powered damage detection module (106) to detect a plurality of container damage types and security seal status, wherein the AI-powered damage detection module (106) utilizes Convolutional Neural Network technique;
extracting container related codes from the container images using the OCR module (108);
generating inspection reports and providing analytics based on the detected container damage types, security seal status, and extracted container related codes using a reporting and analytics processing system (110).
3) The claims of claim 1 or 2, wherein the diverse container image dataset comprises images captured under various lighting conditions, camera angles, and/or container orientations.
4) The claims of claim 1 or 2, wherein the AI-powered damage detection module (106) is configured to detect at least 14 different types of container damage that may include dents, cracks, rust, back side rust, front side rust, upper part rust, push-in, push-out, and corner post cuts.
5) The claims of claim 1 or 2, wherein the OCR module (108) utilizes a proprietary OCR framework to extract container numbers, ISO codes, and vehicle IDs in both vertical and horizontal orientations with at least 97% to 99% accuracy.
6) The claims of claim 1 or 2, wherein the OCR module (108) is further configured to automate Bill of Lading (BoL) and invoice scanning, and maintain digital records of the scanned documents.
7) The claims of claim 1 or 2, further comprising an edge processing capability configured to support real-time analytics and reporting, reducing processing time and operational load on central systems.
8) The claims of claim 1 or 2, wherein the reporting and analytics processing system (110) further configured to maintain tamper-proof, audit-grade logs accessible via an analytics dashboard (114) to ensure data integrity and accessibility.
9) The claims of claim 1 or 2, wherein the reporting and analytics processing system (110) is further configured for continuous real-time monitoring of container’s health using a maintenance predictor (116); and generate container inspection documentation using an automated documentation processing system (118).
10) The claims of claim 1 or 2, wherein the reporting and analytics processing system (110) is configured to generate actionable insights based on the inspection results.
11) The claims of claim 1 or 2, wherein the AI-powered damage detection module (106) further comprises a precise segmentation submodule configured to perform pixel-level segmentation of containers and defects, the segmentation submodule being operable to:
isolate individual containers in multicontainer image frames;
localize and compare container condition changes across yard events for damage assessment and attribution;
delineate defets based on actual contours, enabling measurement of irregularly shaped or diagonal damages; and
convert segmented pixel-based dimensions of the detected damage into real-world units using a dimentional conversion algorithm.
12) The claims of claim 1 or 2, wherein the system (100) further comprises a vision-based container lifecycle management module (120) configured to:
monitor and manage the container’s lifecycle stages;
continuously track and document the container’s health, cargo condition, and associated stakeholders throughout each stage; and
provide end-to-end visibility, accountability, and operational effieciency through vision-based data capture and automated record keeping.
13) The claims of claim 1 or 2, wherein the system (100) further configured to support camera-agnostic, real-time, multi-source container inspection, comprising:
operability across diverse imaging sources including static CCTV cameras, mobile cameras, smartphone camera, and low-cost imaging setups;
capability to operate in a fully automated mode using only smartphone;
ability to capture and process inputs from multiple camera source within 20 seconds using minimal compute resources; and
generation of a comprehensive visual analysis, delivered through a live dashboard and automated PDF-based inspection reports.