# Detection and Classification of Different Weapon Types Using Deep Learning
## 4 Results  
In the study, experiments were conducted for seven different weapon types and the proposed model was compared with the VGG-16, ResNet-50, and ResNet-101 models. In all four models, the dataset that was used in the training of the network was divided into three  
Figure 3: VGG-16 model structure. groups, 60% as training, 20% as testing, and 20% as the validation dataset. This situation is shown in Table 2. The training and testing datasets were used during the training of the network. The success rate of the network was obtained by testing the model with a validation dataset that it had never seen during training. During the training processes in all four models, the parameter values of the activation function (ReLU) [34], mini batch size (32), dropout (0.25) [35], optimization algorithm (Adamax) [36], and epochs (30) were used the same. The input image size for these four model tutorials was set to 144 \(\times\) 144 pixels in gray color format. In all four models, \"accuracy\", \"loss\", \"sensitivity\", and \"specificity\" change graphs determine the success of the network during training, which are shown in Figure 4.  
When the graphs of the VGG-16 model given in Figure 4a were examined, it was seen that the model network did not learn during the first 6 epochs and learned with a 90.12% success accuracy as a result of 30 epochs. Similar results can also be observed in the loss, sensitivity, and specificity graphs. Figure 4b shows that the ResNet-50 model began to learn the model network after the first epoch and reached a success accuracy of 94.25% as a result of 30 epochs. As shown in Figure 4c, The ResNet-101 model reached a success accuracy of 84.43% as a result of 30 epochs and had a lower success accuracy than the ResNet-50 model. In Figure 4d, it can be seen that the model network began to learn after the first epoch for the proposed model and reached a success accuracy of 98.32% as a result of 30 epochs. Similar results can also be observed in the loss, sensitivity, and specificity graphs for this model. The success of the model network depends on the layers and parameter values used in the neural network. In the proposed convolutional neural network model, it is important to process the data and extract the necessary features. For these purposes, the layers and parameter values used in the model are selected according to the model network, which increases the success rate. In the proposed model, the number of layers used was less than for other models, and accordingly the number of parameters was reduced; however, the proposed model is able to process, train, and test data faster than other models. As such, the proposed model appears to have higher success accuracy and a lower loss rate than the VGG-16, ResNet-50, and ResNet-101 models. In addition, with the proposed model, model networks were tested using 1043 weapon images in the network validation cluster to analyze the efficiency of the VGG-16, ResNet-50, and ResNet-101 models, with the \"accuracy\", \"sensitivity\", \"specificity\", and \"loss\" values for all four models given in Table 3.  
When the graphs given in Figure 4 and the values given in Table 3 are examined, it can be seen that the proposed model learned faster starting from the first epochs and had a higher success rate. A confusion matrix was also created to analyze the classification efficacy of the proposed model (Figure 5). According to this graph, the most successful classification rates were obtained for assault rifles at 99.45% and hunting rifles at 99.45%, while the classification rates were least successful for revolvers at 94.62% and bazookas at 97.72%. Although the number of datapoints for the bazooka weapon type was less than available for the remaining weapon types in the dataset, it was still more successfully classified than the revolver type. This can be attributed to the visual similarities between revolvers and pistols in many aspects.