Real Time Weapon Detection using YOLOv8 and Alert Mechanism

Abstract

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention. Here, we present in video a system for tracking weapons suitable for tracking and controlling targets. We use the YOLOv8 (look once) algorithm to detect weapons in live video. YOLO model is an end-to-end deep learning model; it is very popular because it is fast and accurate. Previous methods such as region-based convolutional neural networks (R-CNN) required thousands of network tests to make predictions for an image, which could be time-consuming-Optimization is a laborious and painful process. He focuses on specific areas ofpainting and trains everything personally. The YOLO model, on the other hand, passes the image through the neural network only once. Since speed isimportant in real-time video, we use the YOLOv8 algorithm. This data was trained to classify three groups of weapons (pistols, knives, and artillery). When a weapon is detected, an alert is sent to authorities who can take action and reduce crime before it becomes a crime. Powered by Tensorflow, the system took 294 seconds in the first test toidentify weapon types in 9 categories.