Object Classification Using YOLOv12: A Deep Learning Approach

Object classification has become a fundamental task in computer vision, enabling applications such as autonomous driving, surveillance, and smart healthcare. Recent advancements in deep learning, particularly the YOLO (You Only Look Once) family, have significantly improved real-time object detection and classification performance. This paper presents an object classification system based on the YOLOv12 model, focusing on improved accuracy, speed, and efficiency. The proposed approach is evaluated on benchmark datasets, and experimental results demonstrate superior performance compared to previous YOLO versions. The study highlights the effectiveness of YOLOv12 in real-time applications and discusses future enhancements.