YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

Abstract

Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement cracks.