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@article{181305,
        author = {Shaik Sameer and Shaik Anas and DR.Dara Raju and Rajesh S and Kumari Jelli},
        title = {Detection and Prioritizing for Emergency Vehicle Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4463-4470},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181305},
        abstract = {Emergency vehicle detection systems become optimized by implementing deep learning and visual sensing as part of this project. Emergency vehicles get faster responses through real-time detection that changes traffic light priority to give vehicles priority status. The system evaluates traffic flow to develop open emergency vehicle paths that reduce travel delays. The system develops increased effectiveness through time by using ongoing traffic data learning that lets it adjust to evolving surroundings. A united response to emergencies with optimized efficiency will happen through full integration of this system into current traffic management networks. Urgent management of urban traffic and emergency response functions alongside public safety improvements result from this system that provides precise routing for vehicles.},
        keywords = {Deep Learning, Visual Sensing, Emergency Response Systems, Real-Time Detection, Traffic Management, Emergency Vehicle Detection, Dynamic Traffic Control.},
        month = {June},
        }