Leveraging Deep Learning for Pothole Detection and Road Quality Assessment

Road maintenance is crucial for urban infrastructure, with potholes posing risks to vehicle safety and driver comfort. Traditional pothole detection methods are labor intensive, slow, and prone to errors. To address these challenges, we present a deep learning based pothole detection system leveraging the YOLOv8 architecture, known for its speed and accuracy. Developed in Python with a frontend comprising HTML, CSS, and JavaScript, the system is deployed via the Flask web framework. The detection algorithm, trained on a dataset of 780 images, achieves an accuracy of 71%. It offers three operational modes: image based detection, video processing, and real time detection using a webcam. The image based mode analyzes static images for potholes, while the video mode processes continuous footage for road monitoring. The real time detection mode integrates seamlessly with vehicle systems or roadside monitoring stations. Each mode utilizes the YOLOv8 model to detect potholes efficiently, enabling timely interventions for road maintenance. This project demonstrates the feasibility of deep learning in infrastructure monitoring, highlighting its potential to improve road safety and maintenance efficiency through accurate and scalable detection techniques. Road maintenance is a critical aspect of urban infrastructure management, with potholes posing significant hazards to vehicle safety and driver comfort. Traditional methods of pothole detection are often labor intensive, time consuming, and prone to human error. To address these challenges, we present a novel solution for road pothole detection leveraging deep learning techniques. This project employs the YOLOv8 architecture, a state of the art object detection model known for its high speed and accuracy. Our system is developed using Python, with a frontend composed of HTML, CSS, and JavaScript, and is deployed using the Flask web framework. The detection algorithm was trained on a dataset of 780 images, achieving an overall accuracy of 71%. The detection system is versatile, offering three modes of operation: image based detection, video based detection, and real time detection using a webcam. The image based mode allows users to upload static images, which are then analyzed for pothole presence. The video based mode processes video files, enabling continuous monitoring of road conditions. The webcam mode provides real time detection, making it suitable for integration into vehicle systems or roadside monitoring stations. Each mode leverages the YOLOv8 model to quickly and accurately identify potholes, providing valuable data for timely road maintenance interventions.