GPR Subsurface Object Detection Using Depth-Compensated Voxel Classification
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Solution Overview
Problem
Current ground-penetrating radar (GPR) systems struggle to accurately detect and characterize subsurface objects in real-time, particularly in dynamic environments, as they often rely on direct amplitude analysis and lack effective methods for distinguishing object features like shape and size, which is crucial for applications such as landmine detection and utility mapping.
Innovation Solution
A detection system that processes sequences of GPR images by generating depth-compensated intensities, employing an unsupervised binary classifier to identify statistically significant voxels, and using depth and along-medium compensation to normalize signal attenuation, allowing for the classification and tracking of subsurface objects and their features over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct amplitude analysis is used to detect subsurface objects, then the system is simple and fast, but the accuracy of detecting and characterizing objects is insufficient
Solution Approach 1:
The patent segments the GPR image into multiple depth layers and processes each layer separately through depth compensation. This segmentation allows the system to analyze different depth regions with appropriate compensation factors, improving object detection accuracy without requiring complete reconstruction of the entire 3D dataset, thus balancing complexity and precision.
Solution Approach 2:
The patent applies preliminary depth compensation to the GPR image data before object detection. By pre-processing the data to compensate for signal attenuation at different depths, the system improves measurement precision without adding complex real-time processing requirements during object detection, effectively resolving the contradiction between accuracy and complexity.
2Measurement precision
If complete 3D GPR dataset processing is performed to characterize object shape and size, then object characterization is accurate, but real-time detection capability is lost
Solution Approach 1:
The patent extracts only the essential depth compensation information from the complete 3D GPR dataset and applies it to 2D image processing. This extraction approach allows the system to obtain accurate object shape and size characteristics without processing the entire 3D dataset in real-time, thus maintaining both measurement precision and detection speed.
Solution Approach 2:
The patent applies partial processing by focusing depth compensation on specific depth layers and regions of interest rather than processing the complete 3D dataset. This partial action approach provides sufficient object characterization accuracy while significantly reducing computational load to enable real-time detection.
3Measurement precision
If depth compensation is applied to normalize signal attenuation, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local depth compensation by using depth-specific compensation factors for different depth layers rather than a uniform compensation approach. This local quality approach improves measurement precision for each depth region while keeping the overall computational complexity manageable by processing only the necessary depth-specific corrections.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time detection and characterization of subsurface objects, improving the accuracy of identifying landmines and utilities by distinguishing between different features and changes, thereby enhancing safety and operational efficiency in various applications.
Implementation Method 1
ultra wideband ground-penetrating radar (GPR) antennas... When a radar signal strikes a subsurface object, it is reflected back as a return signal to a receiver
Implementation Method 2
When a radar signal strikes a subsurface object, it is reflected back as a return signal to a receiver
Data Source
AI summary
A detection system that detects subsurface objects within a medium and estimates various features of the objects is provided. The detection system receives a streaming sequence of image frames of the medium at various along-medium locations. An image frame contains voxel values (intensities) representing characteristics of the medium across the medium and in the depth (range) direction. The detection system depth-compensates the intensities for determining which voxels are part of an object using an unsupervised binary classifier. The detection system then connects object voxels into distinct objects and recursively estimates the features of those objects as the image frames stream based on the locations and intensities of the object voxels.