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@article{185678,
        author = {SHAIK SHAHANA ABDUL RASHEED and Dr A. Gautami Latha},
        title = {YOLO-Based Detection of Road Surface Defects from UAV-Captured Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2420-2424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185678},
        abstract = {Road infrastructure maintenance is crucial for safe transportation, but manual data collection is often labour-intensive and risky. In response, we employ UAVs and Artificial Intelligence (AI) to significantly enhance the efficiency and accuracy of road damage detection. Our method leverages three state-of-the-art algorithms, YOLOv4, YOLOv5, and YOLOv7, for object detection in UAV images. Extensive training and testing with datasets from China and Spain reveal that YOLOv7 yields the highest precision.  Furthermore, we extend our research by introducing YOLOv8, which, when trained on road damage data, outperforms other algorithms, demonstrating even greater prediction accuracy. These findings underscore the potential of UAVs and deep learning in road damage detection, paving the way for future advancements in this field.},
        keywords = {Deep Learning, Road Damage Detection, UAV, YOLO.},
        month = {October},
        }