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@article{205480,
        author = {Sarfaraz Sajid Inamdar and Mr. Shakio Akil Halwai and Mr. Kartik Raji Rathod and Mr. Nikhil Dhammasang Kasbe},
        title = {Design and Development of a Deep Learning-Based Pothole Detection System for Smart Transportation Infrastructure},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8602-8612},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205480},
        abstract = {Road surface deterioration, particularly pothole formation, represents a significant challenge for transportation safety, vehicle maintenance, and infrastructure management. Most traditional ways to find potholes involve either manual inspection or sensor-based systems, which are frequently slow, expensive, and not very good at keeping an eye on broad road networks. To overcome these constraints, this research introduces the design and development of a deep learning-based pothole identification system. that utilizes computer vision techniques for automated road damage identification. The proposed framework employs advanced image processing and deep learning models to analyze road surface images and detect potholes accurately in real time. Initially, road images are collected and preprocessed using techniques for example, converting to grayscale, reducing noise, and normalizing to make features more visible. Next, convolutional procedures are used to extract features that show structural problems on road surfaces. A Convolutional Neural Network (CNN) combined with the YOLOv8 object identification framework is employed to detect and categorize potholes by recognizing learnt visual patterns, including cracks, depressions, and texture variations.The model is trained using annotated datasets containing pothole and non-pothole images to improve detection accuracy and robustness under different environmental conditions. The system produces detection results in the form of bounding boxes and confidence scores, enabling clear visualization of damaged road regions. The proposed approach demonstrates improved detection efficiency, real-time processing capability, and scalability compared with conventional inspection techniques. This intelligent system can support smart transportation infrastructure by enabling automated road condition monitoring and assisting authorities in scheduling timely maintenance operations.},
        keywords = {Pothole Detection, Deep Learning, YOLOv8, Computer Vision, Road Surface Monitoring, Image Processing, Convolutional Neural Networks, Smart Transportation Infrastructure, Object Detection, Intelligent Road Inspection.},
        month = {June},
        }