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@article{204503,
        author = {Mohit Popat Nakade and Aniket Mohan Patil},
        title = {Automated Road Safety Evaluation Utilizing Convolutional Neural Networks and Edge Computing A Comprehensive Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2967-2970},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204503},
        abstract = {The rapid degradation of municipal transport infrastructure demands automated structural evaluation frameworks. This research presents the design and implementation of an Artificial Intelligence Smart Road Monitoring System utilizing deep learning architectures for real time pavement anomaly detection. By replacing manual visual inspections, the proposed framework processes video streams captured via vehicle mounted cameras. A fine-tuned object detection network models, detects, and classifies structural surface degradation including potholes, alligator cracks, and longitudinal fractures. Deployed over a decoupled cloud native framework with a high-performance backend, the system stores geo tagged anomaly data into a relational database to generate instant alerts and prioritize maintenance. This end-to-end framework bridges the gap between raw data collection and actionable predictive maintenance strategies for smart city infrastructure management.},
        keywords = {Hawkes Process, Order Book Imbalance, Flash Crash, Anomaly Detection, Limit Order Book, High-Frequency Trading, and Retrieval-Augmented Generation.},
        month = {June},
        }