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@article{196243,
        author = {Sanga Rishi Naath S K and Sastha Abhinav S and Selsiya S and Tamizhselvan K},
        title = {Smart Traffic Grid via YOLOv7},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3901-3910},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196243},
        abstract = {The control of urban traffic experiences growing difficulties as the density of vehicles and intricate traffic mobility increase, which requires real-time surveillance tools. In this paper, a Smart Traffic Detection system is introduced that empowers on the state-of-the-art concept of real-time object recognition that identifies vehicles, pedestrians, and traffic lights with high accuracy. The framework combines detection, logging and visualization in a highly customizable pipeline, generating a continuous log of traces in JSON format to enable traceability and user friendly overlays to enable user feedback. There is stability in the varied traffic conditions and systematic validation proves the scalability in congestion monitoring, traffic rule enforcement and adaptive control policies. Results indicate that the system can demonstrate strong functions in diverse urban environments, which can be useful to traffic authorities. The framework helps in making transport systems safer, efficient and smarter through the combination of rigorous detection and the visualization that is accessible.},
        keywords = {Urban Mobility, Traffic Monitoring, Vehicle Detection, Pedestrian Tracking, Congestion Analysis, Adaptive Control, Intelligent Transportation},
        month = {April},
        }