# Detection and Classification of Different Weapon Types Using Deep Learning
## 5 Discussion  
In this study, a deep-learning-based artificial intelligence model was developed for use in autonomous security control, which can determine whether there are weapons on humans and also distinguish which of the 7 different classes the weapon it detects belongs to.  
Figure 7: Sample test results for different classes: (**a**) assault rifles; (**b**) bazookas; (**c**) grenades; (**d**) hunting rifles; (**e**) knives; (**f**) pistols; (**g**) revolvers.  
The data used in the dataset for the developed model training process were divided into training, validation, and test datasets at certain percentages. In the first stage, the model was trained using the training and validation dataset. In order to test the accuracy of the model trained in the second stage, it was tested using a test dataset that the model could not see and a satisfactory rate of classification success was achieved. In our study, the proposed model identified more types of weapons compared to other studies in the literature. For example, [PERSON] et al. studied an automatic gun detection system for us in videos for surveillance and control purposes. In this study, the pistol was only detected using a pre-trained VGG-16-based classification model [18; 23]. In a previous study, the weapon detection model was improved and only a knife was detected [24]. [PERSON] et al. proposed algorithms to alert human operators when a weapon or knife is detected [29].  
The accuracy of the proposed model (98.40%) has an excellent success rate compared to studies in the literature. For example, [PERSON] et al. developed a system that detects weapons and knives with a success rate of 94.64%, which is lower than the accuracy value in our study [25]. For [PERSON] et al. the success accuracy rate in their study was lower (86%) than the value we obtained [5]. In the real-time object detection study by [PERSON] et al. they detected handheld weapons (pistols and rifles) [20]. A pre-trained VGG-16 based classification model was used and 93% training and 89% test accuracy were obtained [20]. In another study, using the weights of the pre-trained VGG-16 model, knife, pistol, and non-weapon classes were used and the success rate for the weapon classification (98.41%) was similar to our study [26]. Additionally, multiple weapons were detected with a success accuracy rate of 84.26% [30]. [PERSON] et al. focused on identifying weapons in people's hands to detect endangered people. In this study, the model was trained using a dataset containing images of people holding weapons and people not holding weapons, obtaining a success accuracy of 74% [31].  
In addition, our model has also been tested on images of different classes of weapons found on people to assess the real-life availability and classification performance of the developed model. Overall, the newly developed model performed very well, detecting images of weapons against different backgrounds within about 3 s.  
In this study, the importance of the proposed model is increased due to the fact that it is trained and tested on computers that are less expensive and do not have sufficient infrastructure.