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@article{186859,
        author = {Sanskar Bhatkar and Mayank Pande},
        title = {Firearm Detection Using Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3388-3395},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186859},
        abstract = {The escalating global security concerns have necessitated the development of advanced surveillance systems capable of detecting potential threats in real-time. In this context, AI-powered weapon detection systems have emerged as a promising solution to enhance public safety and security. By leveraging cutting-edge computer vision and machine learning techniques, these systems aim to identify and classify weapons within video streams, enabling timely intervention and preventive measures.
The integration of artificial intelligence into surveillance systems has revolutionized the way we monitor and analyze visual data. Deep learning, in particular, has proven to be a powerful tool for object detection and classification tasks. By training deep neural networks on large datasets of annotated images and videos, these systems can learn to recognize complex patterns and features associated with weapons, such as firearms, knives, and explosives.
However, the development of robust and accurate weapon detection systems faces several challenges. One significant challenge is the diversity of weapon types, sizes, and appearances, which can vary across different contexts. Additionally, factors like lighting conditions, camera angles, and occlusions can further complicate the detection process. To address these challenges, researchers have explored various techniques, including feature engineering, data augmentation, and ensemble learning.
This literature review delves into the state-of-the-art techniques, challenges, and future directions in AI-powered weapon detection. It examines the key components of such systems, including data acquisition, model training, and deployment. Furthermore, the review highlights the ethical implications and societal impact of these technologies, emphasizing the need for responsible development and deployment.},
        keywords = {},
        month = {November},
        }