Weapon Detection System Using Deep Learning
Keywords:
Weapon, CNN, Deep learning.Abstract
The main advantage of CNN compared to its
predecessors is that it automatically detects the
important features without any human supervision.
For example, given many pictures of cats and dogs
it learns distinctive features for each class by itself.
CNN is also computationally efficient. Due to its
high recognition rate and fast execution, the
convolutional neural networks have enhanced most
of computer vision tasks, both existing and new
ones. In this article, we propose an implementation
of traffic signs recognition algorithm using
aconvolution neural network. Weapon detection
systems using deep learning have shown promising
results in detecting firearms and other dangerous
objects in surveillance videos and images. These
systems have the potential to improve public safety
by providing realtime alerts to law enforcement
agencies in situations where weapons are present.
However, there are still challenges to be addressed,
such as the need for largescale training data, the
potential for false positives and negatives, and the
ethical considerations surrounding the use of these
systems. Further research and development are
needed to improve the accuracy and reliability of
weapon detection systems and to ensure their
responsible deployment in real-world settings.
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