![]() Thus, it is necessary to detect and evaluate cracks in time at the early stage of their appearance, which enables road structures to become more durable and have a longer service life. Therefore, it is important to monitor the typical signs of road damage, i.e., surface cracks in pavements, in a timely and accurate manner. In China in 2021, the number of traffic accidents was up to 273,098, and the total direct property damage was CNY 145,036,000 in 2022, the Chinese transportation department invested a total of CNY 1.29 trillion in road maintenance. Structural damage to roads may induce serious traffic accidents and substantial economic losses. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91% the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. ![]() Different shooting distances, angles, and lighting conditions are considered. In addition, a road crack dataset containing complex environmental noise is produced. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. ![]() In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. ![]()
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