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

Authors: Jianqiang Liu; Rui Chen; Xiaoyan Zhao; Xingyao Li; Yujie Shang; Peng Geng

Addresses: School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China

Abstract: 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.

Keywords: waterlogging depth detection; deep learning; ellipse fitting; YOLOv5-seg.

DOI: 10.1504/IJISTA.2025.148887

International Journal of Intelligent Systems Technologies and Applications, 2025 Vol.23 No.3, pp.272 - 294

Received: 18 Jul 2024
Accepted: 07 Nov 2024
Published online: 30 Sep 2025 *

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