Road damage detection based on improved YOLO algorithm.

Abstract

With urbanization accelerating and transportation demand growing, road damage has become an increasingly pressing issue. Traditional manual inspection methods are not only time-consuming but also costly, struggling to meet current demands. As a result, adopting deep learning-based road damage detection technologies has emerged as a leading-edge and efficient solution. This paper presents an enhanced object detection algorithm built upon YOLOv5. By integrating CA (Channel Attention) and SA (Spatial Attention) dual-branch attention mechanisms alongside the GIoU (Generalized Intersection over Union) loss, the model's detection accuracy and localization capabilities are strengthened. The dual-branch attention mechanisms enhance feature representation in channel and spatial dimensions, while the GIoU loss optimizes bounding box regression-yielding notable improvements, particularly in small object detection and bounding box localization accuracy. Public datasets are used for training and testing, with pavement distress indices derived from simulated detection calculations. Experimental results show that compared to existing methods, this algorithm boosts the retrieval rate by 2.3%, increases the average value by 0.3, and improves the harmonic mean F1 by 0.7 relative to other models. Additionally, expected pavement evaluation results are obtained through calculating PCI (Pavement Condition Index) values.