Weapon Detection System for Surveillance and Security

Abstract

Weapons are a critical and serious topic and has become a severe threat to current security needs. People who bring firearms into airlines, schools, and other secure locations pose a threat to public safety. In certain regions of the globe, mass shootings and gun violence are on the increase. These kinds of situations are time sensitive and may result in significant loss of life and property. Although CCTVs have been employed in many establishments but these require operators to continuously examines the video streams for weapons. The ability to identify suspicious activity is proportionate to their attention to each video stream shown on the screen, thus leading to a high rate of false positives which can become a liability to the daily operational needs of institutions. Therefore, the requirement for the deployment of video surveillance systems capable of recognizing firearms automatically has increased and plays an important role in intelligent monitoring. Several object detection models are available, which struggle to recognize firearms due to their unique size and form, as well as the varied colours of the background. This thesis presents a comprehensive literature review of recent visionbased approaches for automated detection of firearms from images and videos. The literature has broadly been categorized into classic vision/machine learning based approaches and deep learning based approaches. In this research, we further explored various deep learning alternatives for accurate fire detection. For region based detection, a deep learning based weapon detection system employing YOLO v5 for weapon detection that will be sufficiently resilient in terms of affine, rotation, occlusion, and size. The performance of our system was evaluated on a publicly available dataset and achieved the F1-score of 95.43%. Instance segmentation or pixel level segmentation was also performed which employs Mask-RCNN for the detection and segmentation of firearms. We achieved the detection accuracy (DC) of 90.66% and 88.74% Mean intersection over union (mIoU). The purposed methodology combined both techniques with different preprocessing methods along with various data augmentation techniques to improve the efficiency and accuracy of the system.