Road Crack Detection Algorithm based on Improved YOLOv5s

The current road crack detection models have the issues of insufficient detection accuracy and ineffective detection of small cracks. To address these issues, this paper proposes an improved YOLOv5s road crack detection model. Firstly, the coordinate attention module was inserted after all the C3 modules in the backbone to promote the feature extraction capability. Secondly, we used C2f instead of C3 to strengthen feature fusion. Then the context augmentation module CAM was added before the last Concat to enhance the detection effect of small cracks. Finally, we replaced all but the first Conv module with a Ghost Module to minimize the quantity of parameters and calculations. For convenience, we call this improved model as YOLOv5s-CCCG. The experimental results show that compared with YOLOv5s, the improved model has an improvement of 4.7 and 9% in [email protected] and [email protected]:0.95, respectively. The detection accuracy is higher than several other object detection algorithms.