Authors - Sabid Rahman, Sadah Anjum Shanto, Segufta Nasrin Tamanna, Zurin Alam Aongon, Md. Soadul Islam, Nasirul Islam Abstract - This research suggests a system for the real-time detection of road hazards, specifically potholes, cracks, and open manholes, using deep learning and image processing, and pinpointing the exact geographical location of the defects. These defects can cause road accidents, vehicle damage, traffic congestion, and other inconveniences. To solve these, a YOLOv8m model integrated with the CBAM module was developed for enhanced feature attention and trained on a custom dataset of 2,400 road images containing the three hazard classes. The model achieved a mAP@50 of 82.2%, and the individual class performance scores are 72.2% for potholes, 81.0% for cracks, and 93.3% for open manholes, and a recall of 76.4%, demonstrating reliable performance under varied conditions. An OCR module was integrated with the CBAM-YOLOv8 model to extract GPS coordinates from user-captured photos and videos, and an interactive mapping interface was designed to show and report the exact locations of detected hazards for timely action by authorities.
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