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PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement
Zhen Yang1, Lin Li1,2, Wenting Luo2
1College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350108, China.
Computational Intelligence and Neuroscience
|July 18, 2022
Summary
A new lightweight algorithm, PDNet, enhances asphalt pavement disease detection efficiency by improving accuracy and speed. This method accelerates detection and ensures accurate identification of non-deformation diseases on expressways.
Area of Science:
- Civil Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Accurate and rapid detection of non-deformation asphalt pavement diseases is crucial for expressway inspection efficiency.
- Existing methods often face challenges in model compression, speed, and multiscale detection accuracy.
Purpose of the Study:
- To propose a lightweight algorithm (PDNet) based on improved YOLOv5 for efficient asphalt pavement disease detection.
- To enhance detection speed, accuracy, and robustness for multiscale conditions.
Main Methods:
- Developed a novel cross-layer weighted cascade aggregation network (W-PAN) and economical GhostC3/ShuffleConv modules.
- Utilized CIoU loss, K-Means++ for anchor box clustering, generative adversarial network (GAN) and Poisson fusion for data enhancement, and negative sample training (NST).
- Implemented Softer-NMS for prediction box removal and constructed the FAFU-PD dataset.
Main Results:
- PDNet significantly improved the F1-score by 10 percentage points on the FAFU-PD dataset compared to the original YOLOv5.
- Achieved a 77.5% increase in frames per second (FPS), indicating substantial speed enhancement.
Conclusions:
- PDNet offers a lightweight and efficient solution for asphalt pavement disease detection.
- The proposed improvements lead to superior performance in terms of both accuracy and speed for expressway inspection.
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