Road pothole detection based on improved YOLOv7

Jianli Zhang; Jiaofei Lei

doi:10.1117/12.3000774

9 August 2023 Road pothole detection based on improved YOLOv7

Jianli Zhang, Jiaofei Lei

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127820V (2023) https://doi.org/10.1117/12.3000774
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

According to the World Health Organization, the current global death toll from road traffic accidents is as high as 1.3 million annually. The main cause of road traffic accidents is poor road conditions, and potholes on roads are the most serious type of road diseases. Therefore, timely detection and treatment of road potholes is very necessary. This paper proposes a method based on the use of YOLOv7 deep learning model to detect potholes on the road. At the same time, CBAM attention mechanism and optimization of loss function are added on the basis of this method. Combined with the idea of transfer learning, the improved YOLOv7 network is trained. The final test results are significantly improved compared with other road potholes detection models. F1 score is 78%, Precision value can reach 85.81%, and mAP value can reach 83.02%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianli Zhang and Jiaofei Lei "Road pothole detection based on improved YOLOv7", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127820V (9 August 2023); https://doi.org/10.1117/12.3000774

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