ROADGUARD: AN AUTOMATED ROAD DAMAGE DETECTION AND ASSESSMENT SYSTEM USING YOLOV8
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Supantha Mondal
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Department of Computer Science and Engineering, Techno Institute of Engineering and Management, Formerly Known as Techno Engineering College, Banipur, India
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Abstract
Road surface defects such as potholes and cracks can reduce driving safety and increase maintenance costs when they are not identified early. This paper presents RoadGuard, an automated road-damage detection and assessment prototype that combines YOLOv8-based object detection with interpretable severity and repair-priority analysis. The system accepts a road image through a Flask web interface, stores and previews the selected file, performs YOLOv8 inference, and processes each detected object using separate severity and priority modules. Severity is estimated from the relative bounding-box area and damage type, while repair priority combines severity, damage importance, and detection confidence. In a representative end-to-end test, the system detected one pothole with 84.2% confidence and two cracks with 38.4% and 27.2% confidence. Their computed severity scores were 26.03, 25.06, and 6.83, respectively, while the corresponding repair-priority scores were 53.25, 38.31, and 25.67. The prototype demonstrates how object-detection outputs can be transformed into maintenance-oriented information through a lightweight web application. The reported confidence values are per-detection inference scores, not overall model accuracy. The system is intended as a decision-support prototype and can be extended with segmentation, depth estimation, GPS mapping, larger benchmark evaluation, and field calibration.
Index Terms—Road damage detection; YOLOv8; computer vision; pothole detection; crack detection; severity assessment; repair priority; Flask; intelligent transportation systems
Index Terms—Road damage detection; YOLOv8; computer vision; pothole detection; crack detection; severity assessment; repair priority; Flask; intelligent transportation systems
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Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 8
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Aug 25 2026
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Ghosh, S., Das, A., Roy, S. & Chowdhury, T. R. (2026). RoadGuard: An Automated Road Damage Detection and Assessment System Using YOLOv8. International Journal of Science, Strategic Management and Technology, 02(8), 1-9. https://doi.org/10.55041/ijsmt.v2i8.046
Ghosh, Subhrajeet, et al.. "RoadGuard: An Automated Road Damage Detection and Assessment System Using YOLOv8." International Journal of Science, Strategic Management and Technology, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i8.046.
Ghosh, Subhrajeet,Anurag Das,Souradeep Roy, and Tathagata Chowdhury. "RoadGuard: An Automated Road Damage Detection and Assessment System Using YOLOv8." International Journal of Science, Strategic Management and Technology 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i8.046.
References
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