Seeing Danger: How AI-Based Computer Vision Detects Firearms to Boost Security

This research project explores how computer vision can help save lives by detecting firearms in real time. I built and trained a specialized convolutional neural network with the YOLO/Darknet framework to identify active shooter scenarios with high precision, then integrated the model into a C++ application with computer vision for live video analysis and alerting. The goal is a practical, software-driven approach to faster threat detection. 1


Technologies Used


AI Detection in Action

Real-time firearm detection with 99% accuracy

Neural network training and validation results


Application Interface Gallery

Explore the C++ GUI built with FLTK framework for real-time firearm detection and security monitoring


How it works:

  1. Trained millions of Postive and Negative Datasets for Machine learning task
  2. After Training, deploy the model weights into the C++ program
  3. My C++ program consists of using the OpenCV framework. Using this framework helped me create a clear goal for object detection.

And yup, that works. But not without jumping through a few hoops: