Road surface damage detection based on enhanced YOLOv8

The Road Damage Detection System (RDDS) is crucial in intelligent transportation networks, enhancing driving safety, comfort, and overall traffic efficiency. A key factor in the system's performance is the effectiveness of the underlying detection algorithm. Currently, the YOLOv8 algorithm is widely applied in defect detection, but it faces challenges due to the varying scales of road damage. Specifically, the convolutional downsampling module in the backbone network often has a limited receptive field, reducing its ability to capture global information, while the multi-scale feature fusion network may lose critical local defect details and deep location information. These limitations hinder YOLOv8’s performance in detecting pavement defects. To address these issues, we propose an enhanced algorithm, YOLOv8 with Context Capture and Slimneck Structure (YOLOv8-CCS), which targets multi-scale defect characteristics and the prevalence of small-sized targets in road damage detection. To overcome the limited receptive field and improve global context awareness, we have integrated an enhanced context-guided module downsampling component (E-ContextGuidedBlock_Down), which expands the receptive field and improves context capture. Additionally, we replace the existing multi-scale fusion network with Ghost Shuffle Convolution (GSConv)-Slimneck and introduce the Enhanced VoVNet-based Ghost Shuffle Cross Stage Partial (VoVGSCSP-E) module in specific layers. To further enhance feature extraction and minimize information loss during fusion, we incorporate the Content-Aware ReAssembly of Features (CARAFE) upsampling module and a weighted feature fusion method. Finally, the Multi-Level Context Attention Bottleneck (MLCABOT) module is added between the backbone network and the multi-scale feature fusion network, improving the connectivity and overall feature extraction capability. In validation, our proposed method outperformed YOLOv8 by 3 %, 4.7 % and 3.8 % on the RDD-2022, ROAD-MAS and Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD) datasets, respectively. It also achieved the highest F1 score among comparable detection models and ranked among the top three in inference speed. These results highlight the potential of YOLOv8-CCS for real-time road damage detection, providing a more accurate and comprehensive solution for urban pavement management. Such a system, equipped with an advanced detection algorithm, can significantly improve road maintenance efficiency and enhance driving safety.