ROAD DEFECT DETCTION SYSTEM

This project involves the development of a sophisticated system capable of detecting potholes and line cracks on road surfaces in real-time. Utilizing advanced computer vision techniques and Deep learning algorithms, the system analyzes live video feeds from vehicle-mounted cameras. Each frame is processed to identify and classify road surface anomalies, enhancing road safety and maintenance efficiency. The system employs convolutional neural networks (CNNs) and image processing algorithms implemented in Python using libraries such as TensorFlow and OpenCV. The detection process is optimized to perform under various lighting and weather conditions, ensuring robust and reliable performance. The model boasts a high accuracy rate, demonstrated by its ability to detect subtle and overt road damages effectively, as evidenced by the confidence scores in real-world testing scenarios.