Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks
This addresses public safety by automating weapon detection without human supervision, though it is incremental as it applies existing methods like YOLO and Faster R-CNN with optimizations.
The research tackled the problem of detecting weapons in real-time to mitigate lone-wolf attacks by developing deep learning models, achieving up to 89% average precision with Faster R-CNN and 78% accuracy with YOLOv5 at an inference speed of 8.1ms.
Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.