Deep Learning-Based Pothole Detection for Smart Road Maintenance

Potholes are one of the most common and hazardous forms of road surface damage, posing significant risks to vehicle safety, passenger comfort, and the durability of transportation infrastructure. To address these issues, this study proposes an automated pothole detection system leveraging advanced deep learning methods—specifically, convolutional neural networks (CNNs)—in combination with image processing techniques. In this paper, the CNN model is carefully crafted and optimized to achieve a balance between high detection accuracy and computational efficiency, making it suitable for real-time applications. Experimental results show that the model attains high precision, recall, and F1-score values, indicating strong robustness in identifying potholes across various conditions. The system was tested on a wide range of images from both urban and rural roads.