Stage Object Detection

Stage object detection, particularly one-stage approaches, aims to efficiently and accurately locate and classify objects within images in a single pass, unlike two-stage methods. Current research focuses on improving the accuracy and speed of one-stage detectors, such as YOLO variants and FCOS, through techniques like enhanced feature fusion, semi-supervised learning to reduce annotation needs, and incorporating vision-language models for improved generalization. These advancements are significant for real-time applications in diverse fields, including medical image analysis, autonomous driving, and industrial automation, where speed and efficiency are crucial.