Pothole Detection and Assessment on Highways Using Enhanced YOLO Algorithm With Attention Mechanisms

Economic and social prosperity heavily relies on well‐maintained highways. However, road maintenance faces challenges due to limited funding and resources, with potholes posing significant safety risks. This work introduces a pothole detector designed to detect and estimate pothole areas for timely maintenance. It enhances detection by modifying the YOLO algorithm, using the Xception backbone, and integrating attention mechanisms to improve the prediction of small or clustered objects. Xception’s depthwise separable convolutions enhance feature extraction, outperforming the standard YOLO algorithm in detecting small, irregular potholes and preventing overfitting. The improved YOLO model, along with spatial and channel attention mechanisms, focuses on relevant regions and refines important features specific to pothole areas. Accurate area estimation is achieved through computer vision and traditional segmentation processes. A custom dataset, including the MakeML pothole dataset, a Kaggle dataset, and real‐time footage of Kerala roadways, is used for training and validation. Performance evaluation with mean average precision (mAP) and average precision (AP) metrics shows the pothole detector’s superiority, effectively identifying potholes under various conditions and ensuring safe road infrastructure.