Two Stage Object

Two-stage object detection methods, characterized by a region proposal network followed by a classification and bounding box regression stage, remain a significant area of research in computer vision. Current efforts focus on improving efficiency for on-device inference, enhancing robustness through run-time introspection and uncertainty quantification, and extending capabilities to open-vocabulary detection and challenging scenarios like occluded objects and small objects in aerial imagery. These advancements are crucial for applications ranging from autonomous driving and robotics to retail automation and remote sensing, where accurate and efficient object detection is paramount.