YOLOv5-based pothole measurement

Tao Xue; Xianxin Ke; Qinghua Liu

doi:10.1117/12.2684694

19 October 2023 YOLOv5-based pothole measurement

Tao Xue, Xianxin Ke, Qinghua Liu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092C (2023) https://doi.org/10.1117/12.2684694
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

At present, the detection of pavement diseases mainly focuses on improving the algorithm to improve the accuracy of pavement disease detection, but there are few studies on the measurement of pavement disease size. This paper mainly proposes a new measurement method, which combines the YOLO algorithm with RealSense D415 to measure the depth, and after binarization, dilation and erosion and flood fill of the pothole image, the external rectangle is obtained, and the length and width are calculated by the rectangle pixel difference. The experimental results show that the measurement effect of the pothole size is very good, which can meet the requirements of engineering applications. Among them, the average relative error for length measurement is only 3.58%, followed by width is 7.86%, and the average relative error of depth is slightly larger at 9.38%.

Citation Download Citation

Tao Xue, Xianxin Ke, and Qinghua Liu "YOLOv5-based pothole measurement", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092C (19 October 2023); https://doi.org/10.1117/12.2684694

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