Muhammad Rizwan Anjum

Papers

Total Citations

145

H-Index

About

Muhammad Rizwan Anjum is a leading researcher in computer vision and deep learning, with a primary focus on intelligent surveillance systems and real-time object detection. His most influential work, "Weapon Detection Using YOLO V3 for Smart Surveillance System" (2021), has garnered 145 citations, addressing the critical global challenge of gun-related violence. In this study, Anjum developed a fully automated, computer-based system capable of identifying handguns and rifles using the YOLO V3 architecture, leveraging transfer learning to achieve high accuracy in real-time scenarios. This contribution has significant implications for public safety, enabling proactive threat detection in crowded spaces such as airports, schools, and public events. Beyond this flagship paper, Anjum’s research spans the broader application of deep learning to security and automation, demonstrating how state-of-the-art neural networks can be adapted for practical, life-saving technologies. His work stands out for its direct societal impact, bridging the gap between advanced AI research and deployable solutions. For students and researchers, Anjum exemplifies how focused, application-driven research in computer vision can translate into tangible benefits, making his profile a compelling case study in the power of deep learning for social good.