YOLOv8 and point cloud fusion for enhanced road pothole detection and quantification

Automatic detection of potholes is essential for effective road maintenance and is fundamental to enhancing environmental perception for intelligent transportation systems. Reducing false positives is essential for optimizing detection accuracy in this research domain. This paper introduces a novel method for detecting irregular potholes on road surfaces by integrating depth camera images with point cloud data. The proposed approach utilizes YOLOv8 for initial 2D object detection, identifying candidate regions and corresponding 3D point clouds. The boundary contours of potholes are subsequently determined through surface smoothness analysis, followed by the extraction of all point clouds within these boundaries. To further refine detection accuracy, elevation thresholds are applied to evaluate pothole depth, effectively filtering out false positives such as road surface stains and patches. The experiments were conducted over a 4.7-kilometer road section, demonstrating that on well-maintained road surfaces, the proposed method improves detection accuracy by 6.5% compared to the standalone use of YOLOv8, achieving a precision of 95.8%, a recall of 93.3%, and an F1 score of 94.53%. The model processes a single image in 0.23 seconds. Furthermore, the error rates for perimeter, surface area, and depth detection are limited to within 4%, 5%, and 4%, respectively.