YOLO-RDD: A road defect detection algorithm based on YOLO

Road maintenance is an indispensable part of the transportation system, ensuring road safety and maximizing road efficiency. The rapid and accurate acquisition of road surface information is crucial for effective road maintenance management. With the development of artificial intelligence, computer vision-based road defect detection methods are becoming popular. In this study, we propose a universal road defect detection model named YOLO-RDD, using You Only Look Once version 8 as the basic framework. Firstly, we introduce a new feature extraction and fusion approach to enhance the interaction between shallow and deep feature information, enabling multi-scale defect detection. Secondly, inspired by dynamic snake convolution, as cracks are the major road defects, we propose a new feature extraction module called DSC-C2f to adapt to the elongated and continuous morphology of cracks. Finally, we establish cross-layer connections between the backbone, neck, and prediction stages and integrate a coordinate attention module that considers inter-channel relationships and long-range positional dependencies. This allows the model to selectively focus on relevant parts of shallow and deep semantic information. We evaluate our model using the dataset provided by the crowdsensing-based road damage detection challenge (CRDDC2022) and further assess its performance on a publicly available crack and sealed crack dataset. The experiments demonstrated that our model achieved the best performance compared to the state-of-the-art (SOTA) models. Compared to the baseline model YOLOv8, our model achieved improvements of 1.2% in Precision, 1.9% in Recall, 1.9% in mAP@0.5, 0.8% in mAP@0.5:0.95, and 1.6% in F1-Score.