Abstract: The quality of the road is an important issue that contributes to accidents, resulting in the loss of time, resources, and lives. To manually survey the road issue. This is very delayed and costly. Automatic detection of road conditions facilitates surveys more efficiently than human methods. This research identifies three objects: cracks, potholes, and manhole covers. This research shown the highest efficiency with YOLO V6 compared to YOLO V5, V7, and V8. This paper proposes RoadSCNet, designed for YOLOv6 implementations, has been developed for road research. A key part is the customized Horizon block, which enhances horizontal contextual feature extraction efficacy and reduces the limitations of traditional YOLO architecture in identifying road surface condition by long and low light variation, such as cracks and potholes.

Sujittra Sa-ngiem, Kwankamon Dittakan and Saroch Boonsiripant. “RoadSCNet: Road Surface Condition Detection Network”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161290

@article{Sa-ngiem2025,
title = {RoadSCNet: Road Surface Condition Detection Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161290},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161290},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Sujittra Sa-ngiem and Kwankamon Dittakan and Saroch Boonsiripant}
}