Detection of waterlogging in urban road traffic based on improved YOLOv5-seg and ellipse fitting algorithm

This article proposes an innovative method for acquiring precise waterlogging depth data utilising images from traffic surveillance systems. Initially, the YOLOv5 algorithm identifies the vehicle type and determines its tire specifications accordingly. Subsequently, an enhanced version of the YOLOv5-seg model segments and masks the tire instances, while an ellipse fitting algorithm extracts the geometric parameters of the submerged tires to shape a complete ellipse. With the vehicle tires as benchmarks, a mathematical model for waterlogging depth is formulated, which computes the depth using crucial parameters from the ellipse. The experimental outcomes demonstrate that this algorithm achieves an average localisation accuracy of 96.4%, a mask segmentation accuracy of 95.6%, and maintains a detection error within 5 cm for 90% of the waterlogged depths measured. These findings confirm that the image-based tire detection method for waterlogging measurement is both effective and practical.