Handgun Detection

Handgun detection research focuses on developing automated systems for identifying handguns in real-time video and still images, primarily to enhance security and reduce reliance on human monitoring. Current efforts leverage deep learning, particularly convolutional neural networks (CNNs) and variations like YOLOv5, often incorporating transfer learning and temporal analysis techniques to improve accuracy and speed. A key challenge is addressing the small size, low saliency, and frequent occlusion of handguns in real-world imagery, leading to the development of specialized datasets and evaluation protocols to benchmark algorithm performance. These advancements have the potential to significantly improve public safety and security applications.