Potholes on urban roads pose a significant safety hazard for drivers, cyclists, and pedestrians. Therefore, efficient detection and repair of potholes is crucial for ensuring safe and sustainable transportation infrastructure. In this study, we propose a pothole detection approach using the YOLOv8 object detection algorithm on urban road images. We collected a dataset of urban road images containing potholes and trained our model using transfer learning. We evaluated our model on a test set and achieved an average precision of 0.92 and recall of 0.89 for pothole detection. We also compared our model with other state-of-the-art object detection algorithms, and our approach outperformed them in terms of accuracy and speed. Our proposed approach can be used for real-time pothole detection and management, which can improve road safety and reduce maintenance costs in urban areas.


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    Title :

    Pothole Detection on Urban Roads Using YOLOv8


    Contributors:


    Publication date :

    2023-09-06


    Size :

    299782 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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