Article
Weapon Detection With Surveillance With YOLO
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 control.To 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 real-time detection of weapons. Earlier approaches, such as R-CNN, require multiple region-based evaluations and involve high computational complexity, making them less suitable for real-time 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 well-suited for real-time 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.
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