The safety and security of a region, city, or nation always come first when discussing it. should uphold security and safety for the general public in a variety of settings, such as public spaces like airports. Conventional manual inspection techniques take a lot of time, are ineffective, and are frequently prone to human mistake. Automated weapon detection systems have become a viable way to improve security measures because to developments in computer vision and machine learning. The creation and application of weapon detection systems utilizing deep learning algorithms and computer vision techniques is the main topic of this abstract. The objective is to develop an intelligent system that can recognize weapons in real-time from video or picture streams with accuracy and efficiency. The technology is designed to find and identify many types of weapons, such as knives, guns, explosives, and other potentially harmful items. Preprocessing, feature extraction, classification, and image/video collection are some of the essential parts of the suggested weapon detection system. The system first gathers image or video data from security cameras and other sources. Preprocessing methods are used to improve data quality and lower interference or noise. Convolutional Neural Networks (CNNs) and other feature extraction techniques are then used to extract pertinent features from the input data. By capturing the unique qualities of weaponry, these attributes allow for precise classification. A sizable tagged dataset of weapon and non-weapon photos is needed to train the weapon detection system. This dataset can be used to train the model using deep learning approaches like supervised learning and transfer learning. After training, the model is used in real-time scenarios, processing incoming image or video streams and determining if a weapon is present or not.

convolutional neural network, artificial intelligence, computer vision, deep learning, object detection, and gun detection.