Pothole Boundary Detection Algorithm using Image Segmentation Technique in Urban Road

Abstract

Urban road safety is critically undermined by the presence of potholes, which necessitate accurate detection and assessment for effective maintenance. This study introduces a specialized algorithm designed to precisely detect and delineate the boundaries of potholes using advanced image segmentation techniques. Employing Otsu's method and Canny edge detection, the algorithm seeks to accurately identify and contour the edges of potholes in urban road imagery. The research extends beyond mere detection, focusing on the accurate characterization of potholes, determining their parameters and defining their regions. Implemented in Python and utilizing libraries such as OpenCV, NumPy and Matplotlib, the algorithm is developed to be modular, thoroughly documented and performance optimized. Real-world testing is a cornerstone of this project, with field tests conducted to ensure the algorithm's effectiveness across various road conditions. Additionally, the algorithm’s performance is meticulously evaluated against a diverse set of image datasets sourced from the internet, aiming to validate its robustness and reliability. The study's outcomes demonstrate the algorithm's potential to significantly contribute to road safety and infrastructure maintenance, offering a promising tool for urban road management authorities.