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@article{188434,
        author = {Alyaan Ahmed Ansari and Dr. Ateeq Ur Rahman and Dr. K.M Subramanian},
        title = {Elevating Aerial Inspection: A Comparative Study of YOLO Architectures (v4, v5, v7, and v8) for Road Damage Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2435-2437},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188434},
        abstract = {This project introduces an innovative approach for automated road damage detection using Unmanned Aerial Vehicle (UAV) images and advanced deep learning techniques. Road infrastructure maintenance is crucial for safe transportation, but manual data collection is often labor-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 the other algorithms, demonstrating even greater prediction accuracy. The YOLOv8 model achieved up to 85% accuracy. These findings underscore the potential of UAVs and deep learning in road damage detection.},
        keywords = {Unmanned Aerial Vehicle (UAV), Deep Learning, Road Damage Detection, Object Detection, YOLO (You Only Look Once), YOLOv4, YOLOv5, YOLOv7, YOLOv8, Pavement Monitoring, Artificial Intelligence (AI), RDD2022 Dataset.},
        month = {December},
        }