weapon-detection

Weapon Detection & Classification through CCTV surveillance using Deep Learning-CNNs.

AI-based System for Automatic Detection and Recognition of Weapons in Surveillance Video

AI-driven weapon detection system for real-time surveillance. Developed on TensorFlow, achieved precision of 0.8524 and 0.7006 at IoU 0.50 and 0.75. Utilizes key frame extraction and SSD-MobileNet, enhancing efficiency. Developed on Windows 10, Python 3.7.3, and TensorFlow 1.14.0. Boosts security with low-cost, automated threat recognition.

An AI-powered robotic system designed to detect and identify weapons using advanced computer vision and deep learning techniques. This project combines robotics, machine learning, and real-time detection capabilities for security and surveillance applications.

Basic gun detection algorithm, designed using YOLOv7 with AR-15 guns training data

An AI-Powered System which can detect any type of violence occured in a video file that can be used in CCTV recordings to detect noise and provide necessary alerts.

A compact real-time weapon detection system using Python and YOLOv8. It detects weapons from live streams, triggers customizable alerts, and offers a desktop GUI and Django interface. Powered by a pretrained YOLOv8 model with support for retraining and data collection, its modular design separates the detection server, GUI, and training logic.

Identifying Guns using Machine Learning for Image recognition with a Rapid Response System to Save Lives

weapon detection python opencv with yolov5

Real-time knife and firearm detection on video/stream with YOLOv8 and Roboflow.

A Python-based real-time weapon detection system using YOLO11 and OpenCV, trained on a dataset of 26K+ images across 17 weapon classes. Detects weapons from live webcam/IP feeds, clips footage, and sends time-stamped alerts via Telegram for public safety.

This project proposes a software solution with face recognition and weapon detection to improve perimeter security. Using face recognition algorithms, the system seeks to identify possible invaders into the military bases while concurrently identifying and categorizing any weapons they could be carrying.

A real-time weapon detection system for security surveillance that identifies firearms and distinguishes them from harmless objects. The system processes video input, highlights detected weapons with bounding boxes, and can extract frames containing weapons for further analysis. Built using deep learning (Faster R-CNN with PyTorch) and OpenCV.

AI-powered Workplace Safety Monitoring System using Python, OpenCV, YOLOv8, and Tkinter for real-time Person, PPE, Mobile, and Weapon Detection.

An AI-powered smart border surveillance system built with Python, OpenCV, YOLOv8, and face recognition to detect intrusions, identify known individuals, detect weapons, and generate real-time security alerts.

Package for firearm detection.

End-to-end community security system using YOLOv8, OC-SORT, real-time video streaming, abnormal behavior analysis, and a Blazor dashboard.

AI-powered multi-camera surveillance system for real-time weapon detection, human activity recognition, person tracking, Re-ID, evidence logging, and heatmap analysis.

AI-powered intelligent surveillance system for real-time weapon and violence detection using YOLOv11, OpenCV, and Flask with CCTV integration and automated alerts.

Edge AI weapon detection for CCTV: YOLO on NVIDIA Jetson (TensorRT, DeepStream) runs on-site, with an ASP.NET Core backend, Angular dashboard, live video and real-time alerts.