• KAVYA Y K
• ASHISH L

Advanced weapon identification systems are in greater demand due to the global growth of security concerns. In real-world situations, traditional approaches have not been able to reliably detect concealed weapons. In order to improve weapon recognition capabilities in security systems, this research suggests integrating deep learning approaches with artificial intelligence (AI). In order to achieve superior accuracy and efficiency in detecting different types of weapons, including firearms and bladed instruments, from different camera perspectives and environmental conditions, the proposed system makes use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in conjunction with sophisticated algorithms for feature extraction and classification. This research shows the effectiveness of AI-powered weapon recognition systems in enhancing security protocols and ensuring public safety through extensive testing and assessment.

Artifical Intelligence, CNN, Deap Learning, Faster RCNN, SSD, Weapon Detection, YOLO.

"ARTIFICIAL INTELLIGENCE AND DEEP LEARNING FOR WEAPON IDENTIFICATION IN SECURITY SYSTEMS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i128-i133, March-2024, Available :http://www.jetir.org/papers/JETIR2403818.pdf

"ARTIFICIAL INTELLIGENCE AND DEEP LEARNING FOR WEAPON IDENTIFICATION IN SECURITY SYSTEMS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi128-i133, March-2024, Available at : http://www.jetir.org/papers/JETIR2403818.pdf