G. UMA MAHESWARI,VELETI V N V PRANEETH,M PRASHANTH,MACHARLA SRI LAVAN,SHARIYA MEHROZ 03 03 Statistical evidence indicates a steady rise in violence involving firearms making it increasingly difficult for law enforcement agencies to respond promptly Certain regions particularly those with relaxed gun regulations report higher incidences of crimes involving handguns Ensuring public safety therefore requires the early detection of such threats One effective approach is the use of surveillance systems capable of identifying dangerous weapons like handguns from video footage thereby helping to prevent potential incidents However many existing surveillance systems still rely heavily on manual monitoring and controlTo address this limitation the proposed system enables automatic weapon detection in video streams for monitoring and security purposes The system employs the YOLOv3 You Only Look Once algorithm to achieve realtime detection of weapons Earlier approaches such as RCNN require multiple regionbased evaluations and involve high computational complexity making them less suitable for realtime applications These methods process different regions of an image separately leading to increased processing time In contrast the YOLO architecture processes the entire image in a single pass through the neural network significantly improving speed and efficiency Due to its high processing speed and accuracy YOLOv3 is wellsuited for realtime video analysis When a weapon is detected the system immediately generates alerts to notify authorities enabling timely intervention By facilitating rapid detection and response the proposed system contributes to preventing violent incidents before they occur and enhances overall public safety Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026