Road defect detection based on improved YOLOv8s model
Jinlei Wang, Ruifeng Meng, Yuanhao Huang, Lin Zhou, Lujia Huo, Zhi Qiao, Changchang Niu
Abstract
Road defect detection is critical step for road maintenance periodic inspection. Current methodologies exhibit drawbacks such as low detection accuracy, slow detection speed, and the inability to support edge deployment and real-time detection. To solve this issue, we introduce an improved YOLOv8 road defect detection model. Firstly, we designed the EMA Faster Block structure using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module, and the enhanced C2f module was labeled as C2f-Faster-EMA. Secondly, we improved the model speed by introducing SimSPPF instead of SPPF. Finally, for the head, Detect-Dyhead, chosen to replace the original head, significantly improves the representation ability of heads without introducing any GFLOPs. Experimental results on the road defect detection dataset show that the improved model in this paper outperforms the original YOLOv8, with a 5.8% increase in average accuracy ([email protected]), and notable reductions of 22.33% in model size, 23.03% in parameter size, and 21.68% in computational complexity.