Summary of Real Time Deep Learning Weapon Detection Techniques For Mitigating Lone Wolf Attacks, by Kambhatla Akhila et al.
Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks
by Kambhatla Akhila, Khaled R Ahmed
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a deep learning-based solution for automatic weapon detection without human supervision, aiming to prevent lone-wolf attacks and enhance public safety. The study focuses on designing an efficient neural network that can localize and detect weapon objects, including handguns, knives, revolvers, and rifles, while also detecting people. The proposed models, based on the YOLOv5 and Faster RCNN families, are validated and trained using pruning and ensembling techniques to improve their speed and performance. The results show that the models achieve high scores (78%) with an inference speed of 8.1ms for YOLOv5-based models, while Faster R-CNN models achieve high AP scores (89%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is trying to help keep people safe by using computers to find weapons without a human looking at them. They want to stop bad things from happening, like attacks with guns or knives. To do this, they’re creating special computer programs that can look at pictures and videos and find the weapons in them. These programs are very good at doing this and can even tell what kind of weapon it is and where a person might be. The researchers tested these programs and found that they work really well, which means we might see them used to help keep people safe in the future. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Faster rcnn » Inference » Neural network » Pruning
