Developing a Framework for Weapon and Mask Detection in Surveillance Systems

Abstract

Financial institutions, jewelry stores, hypermarkets and automated teller machines all experience yearly thefts of vast amount of money. Police have dismantled a few of the robbery attempts. Police successfully apprehend most of the robbers. The maintenance of safety and security around the globe is a difficult task for governments, particularly in a country like the UAE, which is home to more than 200 nationalities. This study examines the applications of neural network models in video surveillance systems for detecting weapons, thus preventing robberies. By expanding the dataset to include more classes and photos per class, the proposed model could perform better to be installed on outdoor surveillance systems. In this study, we will examine situations of weapons detectors, develop models using transfer learning approaches, and contrast them with other contemporary detectors like YOLOv5. We will develop our own unique dataset and contrast it with another dataset in terms of classes, image quality, and kind of items used for committing a robbery. Gun detectors in surveillance systems has a wide range of additional uses, from residentials units to the military.