Road to Smooth Journeys: Pothole Detection through Machine Learning

Abstract

Potholes on roads pose significant safety risks and lead to substantial maintenance costs for transportation authorities. Timely and accurate detection of potholes is crucial to ensure the safety of motorists and pedestrians and to efficiently allocate resources for road maintenance. In recent years, machine learning techniques, particularly deep learning algorithms, have shown great promise in various computer vision tasks, including object detection. This paper presents a novel approach for pothole detection using machine learning, specifically Convolutional Neural Networks (CNNs). We collected a diverse dataset of road images containing potholes and non-potholed road surfaces, annotated with ground truth labels. The dataset was pre-processed to enhance feature extraction and minimize noise. Our proposed CNN architecture was designed to efficiently learn discriminative features that enable accurate pothole detection. To evaluate the performance of our model, we conducted extensive experiments using cross-validation and compared it with other state-of-the-art methods. The results demonstrated that our CNN-based approach outperforms traditional image processing techniques and showcases high precision and recall rates in pothole detection. Furthermore, to assess the model's real-world applicability, we deployed it on a real-time pothole detection system using cameras mounted on vehicles. The system successfully detected potholes with remarkable efficiency, opening new possibilities for early warning systems to alert drivers and municipal authorities about hazardous road conditions. This research signifies the potential of machine learning, specifically CNNs, in revolutionizing pothole detection on roads. By automating the process and providing accurate and timely information, this technology can significantly contribute to road safety and optimize maintenance efforts. As autonomous vehicles and smart transportation systems become more prevalent, our work serves as a stepping stone towards creating safer and more efficient road networks for the future.