An automated road defect detection system is a key part of intelligent traffic infrastructure maintenance. Existing object detection models have slow inference speed and low detection accuracy. This problem is more serious under shadows, oil stains, and occlusion from natural environments. Detection accuracy of existing models drops sharply in complex environments. This paper proposes a lightweight and high-accuracy crack detection framework based on YOLOv8s, named Crack-YOLO. It is designed to solve slow speed and low accuracy in road defect detection under complex conditions. The model replaces the original convolution modules in YOLOv8 with Context-Guided (CG) modules. It uses the designed C2fDynamicConv to replace static convolution kernels. The original detection head is replaced with an Adaptive Spatial Feature Fusion (ASFF) head. Experiments and analysis are conducted on four datasets: CrackVariety, CrackTree200, Crack500, and CFD. Experimental results show that Crack-YOLO greatly outperforms YOLOv8s in both detection speed and detection accuracy. On the self-built CrackVariety dataset, the mAP@0. 5 reaches 71. 4%. The inference speed reaches 416 FPS. Compared with the baseline YOLOv8s model, accuracy increases by 31. 0%. The speed increases by nearly two times. In addition, the model is successfully deployed on a Raspberry Pi 5 edge device. It is integrated with the ASTM D6433 standard. After detecting road defects, the Pavement Condition Index (PCI) is calculated automatically. This proves its practicality on devices with limited computing resources.
Fan et al. (Sat,) studied this question.