Authors - Shoib Ahmed Shourav, Shahariar Sarkar, Salekul Islam Abstract - For developing countries, maintaining road network infrastructure is an essential concern. To strengthen a nation’s economy, road infrastructure must be maintained effectively. Potholes, speed breakers, and drain holes in roads are major reasons for causing accidents, traffic jams, and car damage. In addition to improving driving safety and minimizing vehicle damage and accidents, identifying road anomalies like potholes, speed breakers, and drain holes is essential for enabling authorities to effectively manage road maintenance. Self-driving cars need to be able to handle different road conditions. In this research, a custom dataset comprising potholes, drain holes, and speed breakers was developed. The study employs YOLOv11, a cutting-edge deep learning-based object detection model, to accurately detect these anomalies, including a comparison of road anomaly detection performance under daytime and nighttime conditions. The proposed approach achieved an accuracy of 83.8% on the daytime dataset, 81.6% on the nighttime dataset, and 84.4% on the combined dataset.
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