Weapon Classification using Deep Convolutional Neural Network

Increasing crimes in public nowadays pose a serious need of active surveillance systems to overcome such happenings. Type of weapon used in the crime determines its seriousness and nature of crime. An active surveillance with weapon classification can help deciding the course of action while identifying the possibilities of any crime happening. This paper presents a novel approach for weapon classification using Deep Convolutional Neural Networks (DCNN). That is based on the VGGNet architecture. VGGNet is the most recognized CNN architecture which got its place in ImageNet competition 2014, organized for image classification problems. Thus, weights of pre-trained VGG 16 model are taken as the initial weights of convolutional layers for the proposed architecture, where three classes: knife, gun and no-weapon are used to train the classifier. To fine tune the weights of the proposed DCNN, it is trained on the images of these classes downloaded from internet and other captured in the lab. Experiments are performed on Nvidia GeForce GTX1050 Ti G PU to achieve faster and exhaustive training on a large image set. A higher accuracy level of 98.41 % is achieved for weapon classification.