YOLOv8-based improved algorithm for road damage detection

In response to the existing issues of low detection accuracy, susceptibility to environmental interference, and frequent occurrences of missed detections and false alarms in road damage detection algorithms, we propose yolov8n-DS, a road damage detection algorithm based on YOLOv8n. Firstly, to enhance the capability of capturing sparse patterns with large kernels, we improve the Dilation-wise Residual (DWR) module of DWRSeg by leveraging the advantages of DilatedReparamBlock based on UniRepLKNet for feature extraction. We introduce a flexible sampling and feature extraction module, C2F-DRB-DWR. To address the interference of complex backgrounds on damage detection, we adopt Large Separable Kernel-Attention to enhance the original SPPF module's ability to extract global information. In the neck network section, we utilize the aggregation capability within a large receptive field of ContentAware ReAssembly of Features (CARAFE) to improve the neck network's upsampling and enhance computational efficiency. Finally, we introduce a unified Dynamic Head that integrates scale, spatial, and task attention mechanisms to replace the traditional detection head, further improving the network algorithm's generalization capability and overall performance. This paper conducts road damage detection experiments using the aforementioned methods and validates them on the RDD20 dataset. Experimental results demonstrate that the improved algorithm achieves an average precision (map) of 67.1%. The model size is 7.5MB, with a detection speed of 172FPS, showcasing superior comprehensive performance compared to other comparative algorithms.