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@article{160929,
        author = {P Murugan and A Merry Ida and S.Amshiga Brillian and M.Seals Ancy and R Aashika and S.Sneha},
        title = {Weapon Detection System Using  Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {2},
        pages = {104-109},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160929},
        abstract = {Weapon detection is a crucial task in ensuring public safety and preventing violent incidents. In recent years, computer vision technology has been used for real- time weapon detection, and the YOLO (You Only Look Once) object detection algorithm has emerged as a popular and efficient technique for this purpose. In this report, we discuss the use of YOLO for weapon detection, and its performance in identifying firearms, knives, and other weapons. We trained a YOLOv8 model using a dataset of annotated images, and evaluated its performance on a test dataset. The results indicate that YOLO is an accurate and efficient technique for weapon detection, achieving a high map and mA it improves the performance of the model in different scenarios.},
        keywords = {Computer vision, weapon detection, Faster RCNN, CCTV, Machine learning (MI)},
        month = {},
        }