Enhancing Safety and Security: Real-Time Weapon Detection in CCTV Footage Using YOLOv7

Main Article Content

M Bhavsingh
S.Jan Reddy

Abstract

In our relentless pursuit of heightened safety and security, this algorithm harnesses the formidable capabilities of the YOLOV7 deep learning model to achieve remarkable real-time weapon detection within CCTV footage. Leveraging a comprehensive dataset, the algorithm seamlessly processes CCTV frames, a pretrained YOLOV7 model, and a meticulously optimized confidence threshold. The results are striking: with an F1-score of 91 percent and a mean average precision (mAP) of 91.73 percent, it successfully identifies and annotates objects of interest. Post-processing incorporates a confidence threshold, coupled with non-maximum suppression, effectively filtering out objects with low confidence scores. Furthermore, the algorithm offers the flexibility to store frames or activate alerts based on user-defined criteria. The cycle of analysis persists for successive frames, ensuring an uninterrupted real-time vigilance. This algorithm, backed by quantifiable results, demonstrates exceptional promise for significantly enhancing safety and security across a multitude of applications.

Article Details

How to Cite
[1]
M Bhavsingh and S.Jan Reddy, “Enhancing Safety and Security: Real-Time Weapon Detection in CCTV Footage Using YOLOv7”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 6, pp. 1–8, Jun. 2023.
Section
Research Articles

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