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@article{175647,
        author = {Atharv Mhatre and Poonam R Pathak and Om Patil and Sahil Patil},
        title = {Deep Learning for Vehicle Classification and Identification for Security},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3436-3439},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175647},
        abstract = {With the adding demand for intelligence business monitoring and enforcement, deep learning has emerged as an important tool for vehicle classification and identification in security applications. This system presents a comprehensive approach toward processing and recording traffic video footage for vehicle detection, classification, tracking, and identification. This system leverages deep knowledge models for object detection and optical character recognition (OCR) to extract vehicle attributes, including type and license plate number. speed estimation technique caters the tracking of vehicle with accuracy.},
        keywords = {CV; OpenCV; Segmentation; Video Detection; Image analysis, YOLOv8, YOLO v4},
        month = {April},
        }