# Detection and Classification of Different Weapon Types Using Deep Learning
## 2 Related Works  
A study investigating an automatic pistol detection system in videos for surveillance and security purposes [18] created basic training data using the results of deep CNN classification. The best classification model was explored to minimize false positives by evaluating two classification models based on the sliding window and region proposal approaches, and it was seen that the best results were obtained from the faster region-based CNN model [18].  
In an image-fusion-based study [23], imbalance map calculation and selection of candidate regions from input frames were performed. A low-cost, symmetrical dual-camera system was installed to take advantage of this information. According to the results, the number of false positives decreased and the detection of objects in surveillance videos became more convenient [23]. Brightness-guided preprocessing, known as darkening and contrasting at learning and test stages, was proposed to improve detection quality in surveillance videos and a cold steel weapon detection model was presented [24]. In a hybrid weapon detection study using fuzzy logic, a system was developed to detect harmful objects such as guns and knives using additional parameters that improved correct results and reduced false alarm rates [25].  
In a weapon classification study developed using a deep CNN, two new approaches were presented. In the proposed approach, the weights of the pre-trained VGG-16 model were taken. Using this model, the effects of changing the number of neurons in the fully connected layer on classification were investigated [26]. In a study aiming to detect firearms in surveillance videos, the focus was placed only on only areas where human beings were found [5] and a weapon detection system was implemented using the separate components of the weapons [27].  
A study on multilevel security management presented a system for the management of multimedia security in Internet of Things systems. This system automatically analyzed multimedia events and calculated security levels [28]. In another real-time object detection study, the authors detected handheld weapons (pistols and rifles). In that study, a TensorFlow-based application of Overfeat, a CNN-based image classifier and feature extractor, was used to detect and classify weapons in the image [20]. In another study on the automatic detection of firearms and knives, algorithms were proposed to warn human operators when guns or knives were detected in the closed-circuit television system. In the same study, in order to apply the system in real life, the number of false alarms was reduced and a system that was capable of real time warning when a dangerous situation was detected was developed [29].  
Clustering algorithm and color-based segmentation were also previously used to eliminate irrelevant objects from an image for the purpose of automatic visual weapon detection. The Harris detector of interest points and the fast retina keypoint descriptor were used to detect the relevant object (weapon) in segmented images. By applying this system to the collected weapon sample images, the partial jamming, scaling, rotation and presence of multiple weapons were detected [30].  
Another study undertaken to identify people in danger focused on handheld weapons. In that study, by introducing the human object interaction model, methods and systems were established to recognize dangerous events. This system was based on the identification of dangerous objects concealed in possible parts of the human body [31].  
In brief, in the literature, studies have generally attempted to classify concealed weapons, firearms, knives, and handheld weapons; however, to the best of our knowledge, no study has shown the detection and discrimination of different weapon types. The current study proposes a weapon detection model that both detects more weapon classes compared to other studies and has very high accuracy.