IMPROVING WEAPON RECOGNITION FROM IMAGES: APPLICATION OF DEEP LEARNING TECHNIQUE INNOVATION

Abstract

The recognition of weapons from images plays a pivotal role in ensuring public safety and national security. In the current landscape of increasing firearm-related incidents, the need for accurate and efficient weapon detection systems is more crucial than ever. Conventional weapon recognition systems often rely on handcrafted features and traditional computer vision methods, which are limited in their ability to adapt to the diverse range of weapons and environmental conditions. These drawbacks result in reduced accuracy and the potential for false negatives or positives, endangering lives. In response to these challenges, our proposed system leverages state-of-the-art deep learning algorithms to automatically learn discriminative features from weapon images. The proposed system's versatility is demonstrated through its ability to detect various types of weapons, including handguns, rifles, and knives, while also adapting to different lighting conditions and backgrounds. This research represents a significant step towards leveraging machine learning to bolster public safety and security measures, ultimately reducing the potential risks associated with weapon-related incidents.