Deep Learning-based Pothole Detection for Intelligent Transportation: A YOLOv5 Approach

Abstract

Pothole detection plays a crucial role in intelligent transportation systems, ensuring road safety and efficient infrastructure management. Extensive research in the literature has explored various methods for pothole detection. Among these approaches, deep learning-based methods have emerged as highly accurate alternatives, surpassing other techniques. The widespread adoption of deep learning in pothole detection can be justified by its ability to learn discriminative features, leading to improved detection performance automatically. Nevertheless, the present research challenge lies in achieving high accuracy rates while maintaining non-destructiveness and real-time processing. In this study, we propose a deep learning model according to the YOLOv5 architecture to address this challenge. Our method includes generating a custom dataset and conducting training, validation, and testing processes. Experimental outcomes and performance evaluations show the suggested method's efficacy, showcasing its accurate detection capabilities.