Adaptive GPR Image Grayscale Selection for Asphalt Moisture Detection
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Solution Overview
Problem
Current methods for detecting moisture damage in asphalt pavements using ground penetrating radar (GPR) face challenges in accurately positioning and depth determination, relying on manual expertise and focusing on hyperbolic features, which limits their effectiveness and applicability.
Innovation Solution
A method for detecting moisture damage in asphalt pavements based on adaptive selection of GPR image grayscale, involving data collection, preprocessing, and the use of a mixed deep learning model combining ResNet50 for feature extraction and YOLO V2 for target detection, to automatically select and recognize GPR images with appropriate plot scales, thereby overcoming the limitations of manual interpretation and hyperbolic feature focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPR electromagnetic wave method is used for moisture damage detection, then detection capability is improved, but positioning accuracy and depth determination remain insufficient
Solution Approach 1:
The patent introduces GPR image processing as an intermediary step between raw electromagnetic wave data and moisture damage detection. The image processing converts complex radar signals into visual representations that highlight moisture damage regions, thereby improving positioning accuracy while maintaining detection capability
Solution Approach 2:
The patent replaces manual expert interpretation with automated image processing algorithms. The systematic image processing method objectively identifies moisture damage positions and depths, eliminating human subjectivity and improving measurement precision without sacrificing detection reliability
2Measurement precision
If GPR image interpretation depends on expert experience, then detection accuracy may be improved, but application scope is limited and automation is reduced
Solution Approach 1:
The patent enables the GPR system to perform self-interpretation through automated image processing algorithms. The system processes its own output images to identify moisture damage automatically, eliminating dependence on external expert interpretation while maintaining high detection accuracy
Solution Approach 2:
The patent transforms the interpretation process from subjective expert judgment to objective parameter-based analysis. Image processing algorithms analyze quantitative parameters such as signal intensity, reflection patterns, and depth information to automatically detect moisture damage, thereby increasing automation while preserving accuracy
3Measurement precision
If existing methods focus on hyperbolic features, then isolated target detection is improved, but moisture damage detection effectiveness is limited
Solution Approach 1:
The patent develops a universal image processing method that can detect multiple types of pavement defects beyond just isolated targets. The same processing framework effectively identifies reinforcement bars, cracks, and moisture damage by analyzing different feature patterns in GPR images, thereby achieving both isolated target detection and moisture damage detection
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
This approach enables automatic and intelligent detection of moisture damage, providing accurate positioning and pre-maintenance insights, reducing reliance on human expertise and improving detection precision, and facilitating regular inspections by unmanned vehicles.
Implementation Method 1
a GPR uses an antenna to transmit and receive a high-frequency electromagnetic wave to detect material properties inside a medium. The GPR transmits the electromagnetic wave into the ground surface by using a high-frequency and usually polarized radio wave. When the electromagnetic wave hits an object buried under the ground surface or reaches a change boundary of dielectric constant, a reflected wave received by the antenna records a signal difference of the reflection echo.
Data Source
AI summary
A method for detecting a moisture damage on an asphalt pavement based on adaptive selection of a penetrating radar (GPR) image grayscale includes the following steps: step 1: obtaining a moisture damage GPR image dataset through asphalt pavement investigation by using a ground GPR, where a GPR image with an appropriate plot scale is selected according to an adaptive GPR image selection method; step 2: adjusting image resolution, specifically, scaling a resolution of an initial GPR image dataset of a damage directly to 224×224 to obtain a BD dataset; step 3: inputting the dataset into a recognition model, specifically, inputting the BD dataset obtained in step 2 into the recognition model, performing operation by the recognition model, and performing step 4; and step 4: outputting a moisture damage result. The new method truly realizes automatic and intelligent target detection based on the GPR.