YOLO driven dashcam analysis for road surface defect and pothole detection

Atharva Manjunath; Hardik Mavdiya

doi:10.1117/12.3027371

3 October 2024 YOLO driven dashcam analysis for road surface defect and pothole detection

Atharva Manjunath, Hardik Mavdiya

Author Affiliations +

Proceedings Volume 13138, Applications of Machine Learning 2024; 1313810 (2024) https://doi.org/10.1117/12.3027371
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

Car crashes cause approximately 1.19 million fatalities a year worldwide. Hazardous road infrastructure and damaged roads are a leading cause for a large part of these deaths. Traditional road inspection approaches typically involve scheduled maintenance and repair activities over predetermined periods. This paper addresses the problem of identifying road damage by using automated techniques. We utilized footage taken by ourselves and a public dataset with 6359 images to automate defect detection using a YOLOv8 and YOLOv9 object detection model focusing on road damage. This model, validated with a mAP50 (mean average precision) value, shows its effectiveness and ability to run in real-world scenarios. By incorporating new potholes and cracks categories, we enhanced the model’s ability to find specific road defects. This study not only introduces an original and refined dataset but also demonstrates the potential of object detection based on YOLOv8 and YOLOv9 in streamlining road damage detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Atharva Manjunath and Hardik Mavdiya "YOLO driven dashcam analysis for road surface defect and pothole detection", Proc. SPIE 13138, Applications of Machine Learning 2024, 1313810 (3 October 2024); https://doi.org/10.1117/12.3027371

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