Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks

TL;DR

This paper develops real-time deep learning models for weapon detection to prevent lone-wolf attacks, achieving high accuracy and speed using YOLOv5 and Faster R-CNN with pruning and ensembling techniques.

Contribution

It introduces a dual approach using unified and two-stage detectors for weapon and person detection, with optimized models for real-time application.

Findings

YOLOv5 with pruning and ensembling achieves 78% accuracy at 8.1ms inference time.

Faster R-CNN achieves 89% average precision.

Models demonstrate effectiveness in real-time weapon detection scenarios.

Abstract

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%.