  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%.
