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

Iterate.ai has open-sourced a powerful Weapons Detection AI software. The AI was trained on about 100 live guns, plus 20,000 videos of robberies and threats involving weapons. Our engineers taught the AI to detect guns, knives, kevlar vests, and robbery masks.

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

Weapon detection model using YOLOv5 pytorch

The system uses an IP camera for taking inputs. Whenever a weapon is detected, the system alerts the security instantly and prevents any big accident from happening.

MEMEX Weapons Pilot for the illegal weapons domain.

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

A barebone model built for detecting weapons in an image. It was built using YOLOv2 (You Only Look Once Algo version2). The model is built upon Darknet YOLO but is also ported on Tensorflow Lite, Protobuf file and also in YAD2K (Keras port of YOLO).

Weapon Detection

A Real time weapon Surveillance tool supported with the real time feed from cctv / webcam /drone footage,which sends alerts with the detected frame to the nearby stations/user/countys , so as to prevent crimes/murders in the city.This project is open for all.Feel free to send pull requests with new features and improvements.

weapon detection python opencv with yolov5

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

Aims at helping Policemen to identify a potentially dangerous situation like a person holding a deadly weapon and is trained especially for detection of the presence of GUNS in an image.

Video vision transformers for hierarchical anomaly detection in video scenes.

a mini project aiming to detect weapons in real time through cameras and notify authorities in instant

Two neural networks to detect weapons and violence in videos

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.

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.

It is an AI based weapon and intruder detection system using cctv camera on live footage built in Django and Yolo model.