AUTOMATIC WEAPON DETECTION FROM REAL TIME IMAGES AND VIDEOS

AUTOMATIC WEAPON DETECTION FROM REAL TIME

 IMAGES AND VIDEOS

    Mrs.P.Swathi1, M.Kalyani2,  M.Chandrakanth3, Y.Pavan Reddy4,T .Manikesh Goud5

1 Mrs.P.Swathi (assistant professor)

                  2M.Kalyani Department of Computer Science and Engineering (Joginpally B.R Engineering College)

                              3M.chandrakanth Department of Computer Science and Engineering (Joginpally B.R EngineeringCollege)

                           4 Y.pavan Reddy Department of Computer Science and Engineering (Joginpally B.R Engineering College)

        5T.Manikesh Goud Department of Computer Science and Engineering (Joginpally B.R Engineering College)

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ABSTRACT

The main objective of the Project is that Security cameras and video surveillance cameras have become an important part of public safety. However, in many cities, these systems 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 intelligence-based solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun,knives 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. 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.The YOLO model, on the other hand, passes the image through the neural network only once. Since speed is important in real-time video, we use the YOLOv8 algorithm.