Automatic Pothole Detection On Public Roads Using Deep Learning

Automatic Pothole Detection on Public Roads Using Deep Learning is an intelligent system developed to improve road safety by identifying potholes accurately and efficiently. Poor road conditions caused by potholes often lead to accidents, vehicle damage, and traffic congestion. Traditional methods of road inspection rely on manual surveys and public complaints, which are time consuming and not reliable for real time monitoring. This project introduces an automated approach that uses deep learning techniques to detect potholes from road images and video streams. The system captures input through cameras and processes the data using advanced image analysis methods. A Convolutional Neural Network (CNN) along with the YOLO (You Only Look Once) algorithm is used to identify potholes and locate them precisely within the captured frames. The model is trained to distinguish between normal road surfaces and damaged areas, allowing it to detect potholes even under different lighting and environmental conditions. Once detected, the system highlights the potholes and store relevant information for further analysis and maintenance planning. By combining computer vision and deep learning, the proposed system reduces the need for manual inspection and enables real time road condition monitoring. It improves detection accuracy, enhances response time for road maintenance, and contributes to safer and smarter transportation systems.