Diffusion-based road defect detection model integrating edge information and efficient multi-scale convolution

Roads are vital infrastructure components, and the prompt detection and repair of defects are critical for their longevity and safety. This paper introduces the Edge Efficient Multi-Scale Focusing Diffusion Network (EEFNet), a precise method for road defect detection. The Edge Information Enhancement Module (EIEM) accentuates crack contours while minimizing background noise. An Efficient Multi-Scale Convolution (EMSConv) is proposed. The EMSConv captures features across multiple scales, thereby enhancing model efficiency through reduced computational demands and parameter count. The Focusing Diffusion Pyramid Network (FDPN) collects and distributes context-rich features across various scales using a diffusion mechanism, thereby improving detection capabilities. Additionally, the Task Dynamic Align Detection Head (TADDH) facilitates parameter sharing among detection heads, which enhances classification and localization accuracy. EEFNet has demonstrated a 92.7 % accuracy rate at 126 FPS (Frames Per Second) on a road defect dataset and has proven robustness on several diverse datasets including Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD), Visual Object Classes 2007 (VOC2007), Global Road Damage Detection2022 (GRDD2022), and Vision Meets Drone 2019 (Visdrone2019). In addition, by pruning the model and deploying it onto edge computing devices, practical experiments have demonstrated that the EEFNet model has substantial practical application value.