Published Paper Details:

This paper proposes a robust framework for detecting and classifying road surface defects--specifically cracks and potholes - using machine learning algorithms trained on annotated image datasets. High-resolution images of various road conditions are processed and fed into a CNN model, which learns visual features to differentiate between defect types and severities. Traditional inspection methods are labor-intensive, time-consuming, and subject to human error. With the emergence of computer vision and deep learning, particularly convolutional neural networks (CNNs), automated road surface analysis has become a practical solution. The rapid growth of urban infrastructure has made the maintenance of road surfaces a critical issue for city planners and civil engineers. Road cracks and potholes significantly contribute to traffic accidents and long-term infrastructure degradation. The system integrates pre-processing steps like image enhancement, edge detection, and data augmentation to improve detection accuracy under varied lighting and environmental conditions. The trained model achieves high precision in identifying surface anomalies, outperforming conventional techniques. Evaluation metrics such as accuracy, recall, and F1-score are used to validate performance. The proposed method offers scalable deployment options in real-time road surveillance systems through drones or vehicle-mounted cameras. Furthermore, the model supports predictive maintenance planning by pinpointing early-stage defects. This initiative reduces human effort, increases monitoring efficiency, and ultimately enhances road safety. The system's adaptability across diverse geographical terrains further highlights its practicality. With the integration of GPS and cloud storage, defect locations can be mapped and archived for future assessments. This AI-driven approach has the potential to revolutionize road maintenance and traffic safety management globally.