Enhancing Transportation Infrastructure: A Deep Learning Approach for Pothole Detection

Potholes on roads pose significant challenges to transportation infrastructure, contributing to increased accident risks and substantial maintenance expenses. In response to these issues, this research paper presents a novel approach utilizing deep learning techniques for automated pothole detection on roads. An application of Convolutional Neural Networks (CNNs) has been proposed to develop a precise pothole detection model capable of analysing still images of roads. Specifically, the YOLOv8 model has been implemented using PyTorch, a powerful deep learning framework, to design and train the pothole detection system. ‘You Only Look Once’, better known as the YOLO architecture offers efficient object detection on real-time basis, making it suitable for identifying potholes in diverse road conditions. To support model development and evaluation, a comprehensive dataset was leveraged, comprising annotated photos of roads with and without potholes. The efficacy and robustness of the suggested methodology in precisely and resiliently locating potholes are proven through extensive testing and analysis. The trained model exhibits robust performance in detecting potholes of varying sizes and shapes, achieving an average confidence of almost 98% in detecting potholes within images from the validation batch, showcasing its potential for practical deployment in transportation management systems. This research contributes to the advancement of automated infrastructure maintenance practices by harnessing cutting-edge deep learning technologies. The successful implementation of this approach holds promise for improving road safety, optimizing maintenance strategies, and reducing the economic burden associated with pothole-related damages. By showcasing the capabilities of deep learning in pothole detection, this study highlights the transformative potential of artificial intelligence in enhancing transportation infrastructure resilience and sustainability.