Description:Title of The Invention
Deep Learning-based Ground Tunnel Detection Device
Field of the Invention
This invention relates to deep learning-based ground tunnel detection device.
Background of the Invention
US8786485B2: Described are a method and system for detecting and locating changes in an underground region. Changes are detected using a mobile coherent change detection ground penetrating radar (GPR). The GPR system is located on a mobile platform that makes two more measurement passes over the same route to acquire GPR images of an underground region at different times. A lateral offset between the GPR images for the two different times is determined and applied to one of the GPR images to generate a GPR shifted image that is spatially aligned with the other GPR image using a correlation process or other technique. A GPR difference image is generated from the GPR shifted image and the other GPR image. The GPR difference image includes data representative of changes to the underground region that occurred between the two measurement passes. GPR image using a correlation process or other technique. A GPR difference image is generated from the GPR shifted image and the other GPR image. The GPR difference image includes data representative of changes to the underground region that occurred between the two measurement passes.
CN103636015A: An object detection system (24) is disclosed having a transducer (40, 40') for detecting buried objects (26). The transducer is encapsulated within a robust, electromagnetically transparent construction (42).
US8775083B2: A system and method of mapping underground utilities and other subsurface objects involves one or more of acquiring utility location data using a number of different detectors and sensors, processing the multiple detector/sensor output data to produce mapping data, storing the mapping data in a database, and providing access to and use of the stored mapping data by subscribing users on a usage fee basis.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention isrelated to deep learning-based ground tunnel detection device.It is a non-destructive technology that can detect subsurface structures and objects without disturbing the ground.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention relates to deep learning-based ground tunnel detection device. It is a non-destructive technology that can detect subsurface structures and objects without disturbing the ground. It rapidly scan large areas, allowing for efficient detection. Furthermore, it can be used to remotely detect and image subsurface structures and objects, reducing the need for personnel to enter potentially hazardous areas. This is especially useful in military contexts, where there may be landmines, unexploded ordnance, or other dangerous objects buried in the ground. Versatility makes it a valuable tool for military intelligence and planning.

BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1 shows the proposed automatic tunnel detection system. Tunnels and other subsurface constructions that may be utilised for smuggling, infiltration, or other military objectives might be found using ground tunnel detection device. Military personnel can find subsurface constructions because to the radar signals' ability to pass through rock and earth. Ground tunnel detection device may be used to find these potentially harmful objects for both military personnel and civilians. Military personnel can discover and properly dispose of metallic objects buried in the ground because ground tunnel detection device ability to detect their existence. Ground tunnel detection device can be used to find underground infrastructure, including pipelines and wires, which is important to the military. This can aid military personnel in planning and executing missions without causing damage. We have used deep learning models (12) which has the potential to significantly improve the accuracy and efficiency of subsurface imaging and detection. Conductivity sensors (13) are used in providing valuable information about soil moisture, salinity which will give us the idea to detect the areas where GPR data may not be efficient enough. GPR with the help of deep learning model and all other devices installed will help to detect tunnels and other underground structures that may be used for smuggling, infiltration and with the help of GPS will send the coordinates to Computing Unit and display the data to authority through display unit. Thefigures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
In this invention, we have proposed automatic tunnel detection system as shown in Proposed system. Tunnels and other subsurface constructions that may be utilised for smuggling, infiltration, or other military objectives might be found using ground tunnel detection device. Military personnel can find subsurface constructions because to the radar signals' ability to pass through rock and earth. Ground tunnel detection device may be used to find these potentially harmful objects for both military personnel and civilians. Military personnel can discover and properly dispose of metallic objects buried in the ground because ground tunnel detection device ability to detect their existence. Ground tunnel detection device can be used to find underground infrastructure, including pipelines and wires, which is important to the military. This can aid military personnel in planning and executing missions without causing damage. We have used deep learning models which has the potential to significantly improve the accuracy and efficiency of subsurface imaging and detection. Conductivity sensors are used in providing valuable information about soil moisture, salinity which will give us the idea to detect the areas where GPR data may not be efficient enough. GPR with the help of deep learning model and all other devices installed will help to detect tunnels and other underground structures that may be used for smuggling, infiltration and with the help of GPS will send the coordinates to Computing Unit and Display the data to Authority through Display Unit.
ADVANTAGES OF THE INVENTION:
Non-destructive technology that can detect subsurface structures and objects without disturbing the ground.
Can be used to remotely detect and image subsurface structures and objects, reducing the need for personnel to enter potentially hazardous areas. This is especially useful in military contexts, where there may be landmines, unexploded ordnance, or other dangerous objects buried in the ground.
Versatility makes it a valuable tool for military intelligence and planning.
Rapidly scan large areas, allowing for efficient detection.
, Claims:We Claim:
1. Deep learning-based ground tunnel detection device system is comprising with GPR, conductivity sensor, deep learning model.
2. The system is claimed in claim 1, wherein which is consist of deep learning and GPR sensor based system for automatic detection of tunnel in underground.
3. The system is claimed in claim 1, wherein wireless RF communication and solar power based system for wireless detection of tunnel.