Weapon Classification System using VGG16 and Inception ResNet Models
Keywords:
Weapon Classification, Deep Learning, VGG16, Inception ResNetAbstract
Security forces urgently need to implement computerized systems in regard to the increasing number of criminal acts. In the battle against crime, the construction of modern weapon recognizing systems has become crucial. The nature and carefulness of the crime are determined by the type of weapon. In this study, distinct types of weapons classification using deep learning models is presented. The presented approach is developed using the Keras architecture, which is based on the TensorFlow framework, and makes use of the VGGNet and Inception ResNetV2 architecture. The classifier is trained using three classes: knife, gun, and background. The model uses the weapon images from Roboflow. The presented approach outperforms the VGG-16 model (96.25% accuracy) and Inception ResNet-V2 model (97.92% accuracy) in terms of classification accuracy. This study offers a crucial perspective on how well the presented deep learning models handle the challenging issue of weapon classification.
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