Pavement Distress Detection Using Street View Images Captured via Action Camera

Timely and accurately detection as well as rehabilitation of road surface defects are of utmost importance for ensuring road safety and minimizing maintenance cost. However, the variety of pavement distress types and forms makes it difficult to accurately classify and detect them. To tackle the issue, this paper proposes a novel target detection model YOLO-SST based on YOLOv5 with the improvement in pavement distress features. First, a Shuffle Attention mechanism is introduced in the feature extraction backbone network to enhance the detection ability without significantly increasing the computational cost. Secondly, we add a detection layer and embed Swin-Transformer encoder blocks into the C3 module to capture global and contextual information. Finally, to improve the model's detection ability, transfer learning is employed on a self-made dataset called RDDdect_2023, which consists of street view images captured via a DJI Action camera mounted on the car. Experimental results demonstrate that the YOLO-SST model outperforms YOLOv5 and other target detection models in terms of accuracy, recall rate, and mAP@0.5 value for detecting pavement distresses. This confirms that the YOLO-SST model has stronger feature extraction and fusion capabilities, resulting in better detection performance.