Model of pavement pothole target detection with improved YOLOv5s

doi:10.16265/j.cnki.issn1003-3033.2025.01.0619

Abstract

Abstract:

To improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work, reduce the probability of traffic accidents. A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed. This method incorporated the ASFF module into the original YOLOv5s network, replaced the backbone network with the FasterNet network, and further introduced the Efficient Channel Attention (ECA) module. Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model, to verify the target detection effect, and to develop an interactive visualized detection interface. The results show that the improved model accuracy, recall rate, and average detection accuracy have increased by 4.1%, 9.9% and 5.6% respectively. Compared to the original network, the improvement is significant. It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves, thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.

Key words: YOLOv5s, pavement potholes, target detection, adaptive spatial feature fusion(ASFF), FasterNet

CLC Number:

X924.3安全监控系统

ZHAO Jiangping, WANG Xinran, WU Lizhou. Model of pavement pothole target detection with improved YOLOv5s[J]. China Safety Science Journal, 2025, 35(1): 67-74.

Figures/Tables 10

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模型	ASFF	FasterNet	ECA	P/%	R/%	mAP@ 0.5/%	mAP@0.5∶ 0.95/%	FPS/ (帧·s^-1)	浮点运算量/G	参数量/ MiB
YOLOv5s	—	—	—	85.0	70.7	80.9	52.7	176.8	15.8	7.01
YOLOv5s-A	√	—	—	87.3	72.6	81.6	54.6	149.6	24.2	12.45
YOLOv5s-F	—	√	—	83.8	76.2	82.3	53.3	179.9	13.7	5.99
YOLOv5s-E	—	—	√	83.1	77.6	82.6	54.8	175.5	15.8	7.01
YOLOv5s-AF	√	√	—	86.8	76.3	83.1	57.2	154.8	24.2	12.45
YOLOv5s-AFE	√	√	√	89.1	80.6	86.5	59.5	146.0	22.1	11.42