WEAPON DETECTION USING PYTHON-OPENCV

Computer vision-based gun detection is a crucial tool for improving public safety, particularly in high-risk settings like public areas, airports, and schools. The goal of this project is to use Python and well-known machine learning frameworks like OpenCV and TensorFlow to create an automated system that can identify the presence of firearms in real time. The objective is to develop an effective model that can detect firearms in a variety of situations with high accuracy and few false positives. We used a convolutional neural network (CNN) in conjunction with object identification methods to do this, paying special attention to models such as YOLO (You Only Look Once) for quick and precise predictions. In order to enhance the model’s performance, the project also incorporates a data pretreatment pipeline that augments and normalizes images.