# Detection and Classification of Different Weapon Types Using Deep Learning
## 6 Conclusions  
In today's conditions, where criminal activities are increasing, it is very important to detect and recognize whether there is a weapon on a person based on images taken from security cameras, without requiring human intervention. Weapon detection and recognition are important to prevent criminal activities before they occur and so that the appropriate parties can take necessary action. Most criminal activities are carried out using handheld or carried weapons. Handheld or carried weapons are the most important elements used for various crimes, such as theft, illegal hunting, and terrorism. It is necessary to determine the types of weapons that may constitute a criminal element in order to predict whether there will be any criminal element in the images taken from security cameras and to take the necessary precautions beforehand.  
In this study, a new model was developed to detect and classify seven different weapon types based on the VGGNet architecture. In addition, a new dataset consisting of seven different weapon types was constructed. The developed model was compared to the VGG-16 (89.75% success accuracy), ResNet-50 (93.70% success accuracy), and ResNet-101 (83.33% success accuracy) models, showing a very high success rate of 98.40% in comparison. The developed model was also tested using images of weapons carried on human beings. Since the different objects in the tested images were similar to the weapon class, new images were obtained using the region proposal approach. The aspect ratios were rearranged using the coordinates of the new images. The images obtained as a result of the region proposal approach were classified and those belonging to the weapon class were ranked according to success rate to ensure that the model could predict the weapon class located in the region with the highest success rate. As a result of the classification, the weapon coordinates were found in the tested images. These coordinates were placed into a rectangular frame and the weapon class and percentage success rate were determined. The newly developed model showed very good performance by successfully detecting weapon images with different backgrounds.  
In conclusion, the model proposed in this study will contribute to the elimination of many security gaps by increasing the task effectiveness and efficiency of security forces in security applications. In this respect, the fact that the results can be directly transferred to new applications increases the original value of the study. It is expected that the results of the study will lead and contribute to similar studies being undertaken, especially regarding autonomous security units.  
In future studies, an infrastructure investigation could be done on robot soldiers that can automatically monitor and analyze input data and send alerts to security forces in order to process data in real time using security control systems and to increase classification accuracy. In addition, studies should be developed to detect coated guns.  
Conceptualization, V.K., S.T. and A.B.; methodology, V.K., S.T. and A.B.; software, V.K.; validation, V.K., S.T. and A.B.; formal analysis, V.K. and A.B.; investigation, V.K.; resources, V.K.; data curation, V.K. and A.B; writing--original draft preparation, V.K.; writing--review and editing, V.K., S.T. and A.B.; visualization, V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The labeled dataset used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
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