Road Pothole Detection Algorithm Based on Improved YOLOv5s

doi:10.3969/j.issn.1006-2475.2023.06.012

Abstract

Abstract: Aiming at the problem that existing target detection algorithms are difficult to accurately detect road potholes and the detection speed is slow, a road pothole detection algorithm based on improved YOLOv5s is proposed. Firstly, CA （Coordinate attention） module is integrated into YOLOv5s backbone network, so that the model can capture not only cross-channel information, but also direction perception and position sensitive information, which is helpful for the model to locate and identify the detected object more accurately. Then, SoftPool is adopted in Spatial Pyramid Pool （SPP） module to improve the maximum pooling operation and retain more detailed characteristic information. In the feature fusion stage, Content-Aware ReAssembly of FEatures （CARAFE） is used to improve the up-sampling of multi-scale feature fusion and dynamically generate an adaptive kernel, which can gather context information in a large receptive field. Finally, Alpha-IoU is used to improve the loss function and improve the margin regression accuracy. Experimental results show that the average accuracy of the improved YOLOv5s algorithm is 4.6 percentage points higher than that of the original network, and the detection accuracy of the improved YOLOv5s algorithm is greatly improved compared with other mainstream algorithms such as SSD, Faster R-CNN, YOLOv3, YOLOv3-tiny and YOLOv4-tiny.

Key words: deep learning, pothole detection, coordinate attention, maximum pooling

CLC Number:

TP391

BAI Rui, XU Yang, WANG Bin, ZHANG Wen-wen. Road Pothole Detection Algorithm Based on Improved YOLOv5s[J]. Computer and Modernization, 2023, 0(06): 69-75.

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