Abstract
The detection of road surface anomalies is a crucial task for modern traffic monitoring systems. In this paper, we used the YOLOv8 network,- a state-of-the-art convolutional neural network architecture, for real-time object recognition and to automatically identify potholes, cracks, and patches on the road surface. We created a custom dataset of 1044 road surface images in Vietnam, each of which was annotated with pavement anomalies, and the YOLOv8 network was trained with this dataset. The results show that the model achieved an accuracy of 0.56 mAP at a threshold of 0.5, indicating its potential for practical application.
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