Smart Surveillance: Multi Weapon Armed Person Detection With Automated Threat Alerts
DOI:
https://doi.org/10.63949/Keywords:
- Deep learning,
- armed weapon detection,
- machine learning,
- object detection,
- Convolutional neural networks
Abstract
The rise in criminal activities has created an urgent demand for intelligent surveillance and command systems in law enforcement. This work introduces a deep learning–based model tailored for classifying distinct weapon categories. The model is developed on the YoloV8-CNN+LSTM framework and implemented using Keras with TensorFlow as the backend. It is trained to recognize gun, rifles, knives. A carefully curated dataset containing 5,214 images was prepared for training and evaluation. The proposed network underwent comparative testing against established models, including VGG-16, ResNet-50, and ResNet-101. Results show that the model achieved an improved accuracy of 92\%, outperforming VGG-16 (89.75\%), ResNet-50 (90.70\%), and ResNet-101 (83.33\%). These findings helps to improve proposed approach in enhancing weapon detection accuracy, thereby strengthening the capabilities of security forces in preventing crimes.
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References
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