Abstract
Object detection and classification on digital images have great importance in the digitalizing world. After deep learning methods started being implemented for object detection, classification and segmentation a rapid development has been monitored in the field. One of the most successful methods in the field is Mask R-CNN. It can be used in order to detect and segment purposes for many different objects. This study contains the use of Mask R-CNN for weapon detection, specifically handguns. Nowadays, there are many cameras in public areas and these cameras can detect weapons before a forensic incident. Our model achieved a mean average precision (mAP) of 0.78 in the detection of handguns on test data. Our findings verify the potential of deep learning in security by detecting threats in images and live videos.
Published Version
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