# Detection and Classification of Different Weapon Types Using Deep Learning
###### Abstract  
Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolves. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model's success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.  
deep learning; convolutional neural network; VGGNet; ResNet; weapon detection +
Footnote †: journal: Medical  
image segmentation. To date, CNNs have achieved the best results for classical image processing problems, such as image segmentation, classification, and detection [15; 16].  
Today, most criminal activities are carried out using handheld weapons [16]. Many studies have revealed that handheld weapons [17] are the most important criminal elements used for various crimes, such as theft, illegal hunting, and terrorism. The solution to reduce such criminal activities is installing a surveillance system [18; 19] or control cameras [20] so that security units can take appropriate measures at early stages [16]. Weapon detection is challenging due to the various subtleties associated with it. The most important problems in weapon detection are self-occlusion and the similarities between objects and background structures. Self-occlusion occurs when a part of the gun is blocked on one side. Similarity between objects occurs when different objects such as hands and clothes look like weapons. Background problems [15] refer to those related to the background against which the gun is located [16].  
This study proposes a new model to detect seven different weapon types (assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers) based on the deep learning method. In order to evaluate the classification performance of the proposed model, it is compared to the Visual Geometry Group (VGG-16) model [21] and Residual Network (ResNet50 and ResNet101) models [22], which are accepted in the literature. As a result of this comparison, it is seen that the proposed model has higher accuracy and a lower loss rate than the VGG-16, Resnet-50, and Resnet-101 models.  
The remainder of the study is organized as follows. In Section 2, the literature on the subject is presented. The material and methods are explained in Section 3 and the results are presented in Section 4. In Section 5, the discussion is presented. Lastly, Section 6 concludes the study.