Description:Field of Invention
The recommended invention is a contemporary “AI-powered Traffic Helmet Risk Detection” system that will automate the process of detecting whether the person is wearing a helmet or not, and if there are any accidents or collisions which eventually reduces manpower and makes the job of police easy to track people. This full system captures the person who is not wearing a helmet if not the model is capable of extracting the vehicle’s number plate and automatically sends alerts to the police additionally it sends the message alerts using Twilio by mapping the license plate number to the registered mobile number of the vehicle. The system offers automatic bike rider tracking in traffic and accident detection, which supports a safe environment. Its adaptability implies that it adjusts to shifting Traffic needs, redefining Rider monitoring.
Objective of This Invention
A total of 46,593 persons who were not wearing helmets were killed in road accidents last year, according to a report by the Ministry of Road Transport and Highways. Of these, 32,877 were drivers and 13,716 were passengers to tackle this problem we built “An AI-powered Traffic Helmet Risk Detector” whose principal purpose is to revolutionize helmet risk identification in an advanced way. The system seeks to give real-time information about who is wearing a helmet and who is not wearing a helmet, accidents, riders’ license plates, and arguments by utilizing AI algorithms and hardware integration. At the same time, it streamlines the messaging and sends alerts via automated procedures. The invention also intends to enhance Rider safety as well as pedestrian safety by immediately recognizing safe and unsafe people in traffic and giving out real-time warnings to vehicle owners as well as police. This multimodal method offers people effective intervention tools.
Background of The Invention
In the bustling streets of India, the decision to wear a helmet while riding a two-wheeler extends far beyond personal choice; it's a matter of life and death. The consequences of neglecting this safety measure are severe, impacting not only the individual but also their loved ones
For instance, US10296794B2 outlines an artificial intelligence-driven system with diverse modules for processing, detection, training, logic, annotation, and more. It emphasizes the invention’s flexibility, suggesting it can take various forms beyond the embodiments presented. The patent underscores the integration of on-demand services, crowdsourced stewardship systems, and PARKSAFE technology for parking allocation. A notable aspect is the system's ability to connect specialists, auditors, and data collectors via the cloud, facilitating a comprehensive solution for processing motor vehicle violations. This innovative approach enables efficient violation detection, management, and data handling, demonstrating the potential for enhanced roadway stewardship and parking management.
CN106372662A patent describes a method and device for detecting the wearing of safety helmets using artificial intelligence. The method involves obtaining images from live video and detecting human positions to determine if they are within helmet-wearing areas. If so, the system detects whether helmets are worn and assesses their type, ensuring compliance with regulations. Additionally, the patent covers the training of detection models using training data and markup information to improve accuracy. The device includes modules for region detection, safety helmet state detection, and safety helmet type detection. Overall, the invention aims to enhance safety by accurately verifying the wearing of safety helmets in various environments.US9368028 presents processes to enhance vehicle safety by providing threat information to users and guiding them to take appropriate actions. Key points include directing users to adjust their driving behavior based on threat information, such as decelerating, turning, or accelerating. Emphasis is placed on minimizing overall costs associated with threats, including social costs. Threat information is conveyed through visual and audio outputs, utilizing components such as the Agent Logic for intelligence processing, the Presentation Engine for displaying warnings, and Information Fusion for integrating data from various sources to assess risks. Overall, the document underscores the importance of leveraging technology to improve decision-making and mitigate potential dangers on the road.
The patent US10034066 describes various embodiments of smart devices designed for different applications, such as gaming, oral hygiene monitoring, sports analysis, and fitness tracking. These devices include smart gloves, patches, clothing, and handles for sports equipment. They incorporate sensors, processors, wireless communication modules, and other components to detect, record, and analyze various data such as acceleration, gestures, and environmental factors. The devices are capable of monitoring user actions, providing feedback, and even simulating conditions for educational or entertainment purposes. Additionally, they can communicate with external devices such as smartphones for data storage, analysis, and display.The patent US6546119B2 outlines a system for monitoring and reporting traffic violations using an enforcement camera system integrated with a remote data processing system. The cameras capture synchronized high-resolution images of vehicles, scenes, and drivers, which are processed to extract and enhance critical details like license plates and driver information using advanced image processing and optical character recognition (OCR). The system ensures secure evidence handling with encryption and signed property information for prosecution. It also includes adaptive image analysis to improve detection accuracy over time and generates verified violation notices for law enforcement and drivers. This innovation enhances traffic enforcement with automation, precision, and robust evidence management.
Summary of the invention
The presented invention is an “AI-powered Traffic Helmet Risk Detection” designed to revolutionize Traffic analysis and incident detection on the roads. The invention, AI-Powered Traffic Helmet Risk Detection System, is designed to automate the detection of helmet non-compliance among two-wheeler riders and streamline the reporting process. Utilizing computer vision, machine learning, and communication technologies, the system identifies riders without helmets, extracts their vehicle number plates using Optical Character Recognition (OCR), and sends automated alerts via Twilio to both law enforcement authorities and the vehicle owners. Additionally, it logs violations, generates incident reports, and supports accident detection. This system improves road safety by reducing manual intervention, enhancing enforcement efficiency, and fostering compliance with helmet laws
Brief description of drawings
The invention will be described in detail concerning the exemplary embodiments shown in the figures wherein:
Image Capture and Processing: The system begins by capturing an image or video frame as input and It preprocesses the input, converting it to grayscale and isolating the region of interest (ROI) where riders are likely to appear.YOLO Model Detection: A YOLO model identifies bounding boxes (B Boxes) around riders and potential helmets within the image.Helmet Recognition: Each bounding box is processed to classify whether the rider is wearing a helmet or not. If the rider is wearing a helmet, the image is discarded.If not, the system isolates the vehicle's license plate for further analysis.License Plate Recognition: The bounding box for the traffic plate is passed to an OCR system, which extracts the text (license plate number).Data Logging and Alerting: For riders without helmets, the extracted license plate number and associated images are stored for further actions, such as generating alerts or issuing fines.
Detailed description of the invention
The AI-Powered Traffic Helmet Risk Detection System is an advanced technological solution for enhancing road safety and automating the enforcement of helmet compliance laws. It leverages real-time video analysis through strategically placed CCTV cameras to monitor traffic flow and identify two-wheeler riders. The system employs OpenCV for initial image preprocessing, isolating regions of interest to detect riders and their attributes. The detection pipeline uses the YOLOv8 model, a state-of-the-art object detection algorithm known for its speed and accuracy, to determine whether riders are wearing helmets. The model is trained on a comprehensive dataset of images, ensuring robust performance under various lighting and environmental conditions. For violations, the system initiates a secondary process of license plate recognition using PaddleOCR, a highly efficient optical character recognition framework. This module extracts the vehicle's license plate number from the captured frames and cross-references it with a centralized vehicle registration database. The integration of OCR technology ensures that even partial or obscured license plates are accurately identified, making the system reliable in diverse traffic scenarios. The extracted license plate information is then used to fetch the registered mobile number of the vehicle owner, facilitating seamless communication for further actions.The communication framework is powered by the Twilio API, enabling automated alerts to be sent to both traffic authorities and vehicle owners. These notifications include critical details such as the rider's violation image, location, and timestamp. In cases of helmet violations, an e-challan is generated and sent directly to the vehicle owner's registered mobile number. The images of violations are uploaded to an Amazon S3 bucket for secure storage and accessibility by authorized personnel. The system also supports the logging of incidents in a database for creating statistical insights into helmet compliance patterns, high-risk areas, and traffic trends.
Designed with scalability and adaptability in mind, the system can be implemented in various traffic environments and scaled across multiple jurisdictions. Its modular architecture supports additional features like accident detection, crowd density analysis, and rider activity tracking. This holistic approach not only automates helmet compliance enforcement but also fosters a safety-conscious culture among riders, aiding in the reduction of road traffic injuries and fatalities. The describes an automated license plate recognition (ALPR) system and method utilizing a human-in-the-loop adaptive learning approach. It processes images of vehicles to identify license plates using an OCR engine trained on a dataset of license plate images. A confidence level is generated for recognized character sequences, and low-confidence cases are routed to human operators for validation. This human input is incorporated to adjust OCR algorithm parameters and enhance its recognition accuracy over time, accommodating variations in plate designs, environmental conditions, and image quality. The system supports applications across diverse transportation scenarios, integrating noise reduction, deblurring, and advanced filtering techniques to improve performance. The describes system and method for dynamic image recognition provide a sophisticated approach to object inspection, utilizing image recognition software that processes raw image data to identify and classify defects on objects like printed wiring assemblies. It segments regions of interest, applies spatial image transforms, and generates derived spaces to extract and score features for decision-making based on a knowledge base. This includes presence/absence classification, polarity assessment, and defect subclassification with confidence calculations. The software incrementally updates its knowledge base for improved accuracy over time, enabling real-time defect detection and classification. Key functionalities include preprocessing for image quality enhancement, clustering for feature extraction, and decision pruning to optimize the recognition process. System Architecture: The AI-powered Traffic Helmet Risk Detection system comprises several key components CCTV Cameras Strategically placed along roads and intersections. Capture real-time video footage of traffic. Image Processing Unit Uses OpenCV for initial image preprocessing. Detects human presence and identifies riders on two-wheelers. Helmet Detection Module Utilizes Convolutional Neural Networks (CNN) and the YOLO (You Only Look Once) v8 model. Processes images to determine if the detected riders are wearing helmets. Trained on a diverse dataset of helmet and non-helmet images to ensure high accuracy. License Plate Recognition (LPR) Unit Employs Optical Character Recognition (OCR) to read vehicle number plates. Integrates with national vehicle registration databases to map license plates to registered mobile numbers. Alert System Uses the Twilio API for sending SMS alerts. Generates real-time alerts to traffic police and vehicle owners, including images of the violation. Incident Reporting Module Logs incidents of helmet non-compliance. Compiles data for generating reports and statistics on helmet usage and traffic violations. Functional Workflow: Image Capture CCTV cameras continuously capture traffic footage. Rider Detection the Image Processing Unit scans the footage for the presence of two-wheeler riders. Helmet Verification: The Helmet Detection Module analyzes the detected riders. Identifies whether each rider is wearing a helmet. Violation Detection If a rider is found without a helmet, the system flags the image for further processing. License Plate Recognition the LPR Unit reads the number plate of the vehicle. Cross-references the plate number with the vehicle registration database. Alert Generation the Alert System sends an SMS to the registered mobile number of the vehicle owner. Notifies the traffic police with the details of the violation, including the image of the rider and the vehicle’s number plate. Incident Logging: The Incident Reporting Module logs all detected violations. Generates periodic reports for traffic authorities to analyze trends and effectiveness. Technological Components: Machine Learning Algorithms: Convolutional Neural Networks (CNN) for object detection. YOLO v8 for real-time detection and classification. Image Processing Techniques: OpenCV for preprocessing and feature extraction. OCR for reading license plates. Communication Protocols: Twilio API for SMS notifications.
Advantages of the proposed model, Improved Road Safety: By automatically detecting whether individuals are wearing helmets while riding two-wheelers, the system contributes to reducing road accidents and fatalities caused by head injuries. This directly aligns to enhance road safety and reduce the number of casualties.Efficiency and Automation: The system automates the process of detecting helmet violations, thus reducing the manual effort required by traffic police. This leads to more efficient enforcement of traffic laws and allows law enforcement agencies to focus on other aspects of traffic management.Real-time Alerts: The ability to send real-time alerts to both the police and vehicle owners ensures swift action can be taken in cases of helmet violations. This facilitates immediate intervention, potentially preventing accidents and improving overall road safety.Enhanced Enforcement: By integrating features like Optical Character Recognition (OCR) and Twilio for sending alerts, the system enhances enforcement capabilities. It not only detects violations but also ensures that appropriate action is taken, such as issuing e-challans to vehicle owners. Scalability and Adaptability: The system can be scaled to cover larger areas and adapted to different environments, making it suitable for deployment in various cities and regions. Its flexibility allows for customization according to specific traffic management needs.Reduction in Traffic Violations: With the implementation of this system, there is a potential decrease in traffic violations related to helmet non-compliance. This contributes to overall traffic discipline and a safer road environment for all users. , Claims:The scope of the invention is defined by the following claims:

Claims
1. Automated Traffic Helmet Violation Detection and Reporting System for Law Enforcement and Vehicle Owners comprising
(a) Strategically placed CCTV cameras along the roadside are solely responsible for detecting the people who are not wearing helmets (Both the driver and passenger as well).
(b) The proposed model can detect more than two people traveling in a two-wheeler.
(c) It can predict the quality of the helmet as well (Nowadays all are using only half-covered helmets in place of the covered rounded helmets).
2. As per claim 1, It incorporates the features of Optical Character Recognition(OCR) and Twilio to send alerts by accurately predicting the characters on the number plate of the vehicle.
3. As per claim 1, once the detected event is labeled as abnormal the model will automatically send alerts to higher authorities along with the captured image through CCTV.
4. According to claim 1, the model is also capable of initiating an E-Challen to the respective owner of the bike with the help of a registered mobile number along with the image of the incident.