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Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion
Xiaowei Jin1, Mingxing Gao2, Danlan Li2
1School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, Inner Mongolia, China.
Plos One
|March 15, 2024
Summary
An improved U-net model enhances highway waveform guardrail recognition, boosting accuracy and efficiency for road maintenance. This advanced system effectively detects guardrail defects and deformation, improving overall infrastructure safety.
Area of Science:
- Computer Vision
- Road Infrastructure Maintenance
- Machine Learning
Background:
- Current highway waveform guardrail recognition faces challenges with low segmentation accuracy and noise interference.
- Efficient and accurate detection is crucial for timely road maintenance and safety.
Purpose of the Study:
- To propose an improved U-net semantic segmentation model for enhanced highway waveform guardrail recognition.
- To increase the efficiency and accuracy of road maintenance detection systems.
Main Methods:
- An improved U-net semantic segmentation model incorporating mixed expansion convolution and a mixed loss function.
- Utilizing partial mean gray values in ROI for guardrail shedding detection.
- Applying first-order detail coefficients of wavelet transform for defect and deformation detection.
Main Results:
- The improved model achieved an 8.63% increase in Mean Intersection over Union (Miou) and a 17.67% increase in Dice coefficient compared to traditional models.
- Defect detection accuracy exceeded 85%.
- Significant improvements in segmentation accuracy and noise interference reduction were observed.
Conclusions:
- The proposed improved U-net model offers a more efficient and accurate solution for highway waveform guardrail recognition.
- This advancement shortens detection processes and enhances the effectiveness of road maintenance operations.
