Vehicle-Mounted Road Damage Detection Method Using YOLOv5s Framework

Timely detection and accurate recognition of road damage are crucial for road maintenance and traffic safety. Traditional road damage inspection methods, such as manual inspection and manual annotation, are inefficient and costly. Therefore, computer vision methods have become a faster and more cost...

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Vydáno v:Proceedings (International Conference on Communication Technology. Online) s. 204 - 209
Hlavní autoři: Qian, Wenjie, Chen, Si, Huang, Youxiang, Xu, Xiaohu, Shi, Feng, Wan, Hong, Lu, Zhiyu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 20.10.2023
Témata:
computer vision
Deep learning
Inspection
Manuals
Object-detection algorithms
road damage detection
Roads
Safety
Training
Urban areas
YOLO
YOLOvSs
ISSN:2576-7828
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Popis
Shrnutí:Timely detection and accurate recognition of road damage are crucial for road maintenance and traffic safety. Traditional road damage inspection methods, such as manual inspection and manual annotation, are inefficient and costly. Therefore, computer vision methods have become a faster and more cost-effective choice. In this study, we collected a dataset called RoadBHD, which consists of 4,579 images and 5,005 labels, including six types of road damage. We built a low-cost and efficient vehicle-mounted road damage detection system based on the YOLOv5s network model. Through experimental comparisons, the YOLOv5s network demonstrated superior performance in road damage detection compared to Faster R-CNN and SSD networks. This validates the feasibility of our designed vehicle-mounted road damage detection system.
ISSN:2576-7828
DOI:10.1109/ICCT59356.2023.10419778