Gun and knife-related crime is a serious issue in the UK that demands immediate action.
Current surveillance systems are limited in their ability to quickly identify potential threats in …

In modern safety surveillance systems, the effective detection of unanticipated incidents in
challenging conditions remains a critical challenge, particularly in environments with poor …

Our research employs cutting-edge deep learning techniques to automate the detection of
weapons and violent activities in CCTV footage. By utilizing advanced deep learning …

Video surveillance is essential for creating a secure and hassle-free environment in all
areas of life. It helps identify theft, detect unusual events in crowded locations, and monitor …

Our groundbreaking research introduces a state-of-the-art security framework that
revolutionizes real-time weapon recognition in surveillance videos. By combining the cutting …

In this research, we advance the domain of public safety by developing a machine learning
model that utilizes the YOLO v8 architecture for real-time detection of firearms in video …

The threat of violence involving guns and knives presents a persistent challenge to global
public safety, necessitating effective detection systems despite the small size and potential …

The principal goal of this study is to create a crime detection system in real-time that can
effectively handle closed-circuit television (CCTV) video feeds and evaluate them for …

Handguns, pistols, and revolvers are commonly used in today's world for committing criminal
acts, requiring the need for effective surveillance and control systems. However, despite the …

Deep learning (DL) algorithms have found extensive applications in the realm of intelligent
policy contexts. In specific environments like waiting rooms and interrogation rooms, where …