Oriented Object Detection

Oriented object detection focuses on accurately identifying and locating objects with arbitrary rotations in images, particularly prevalent in aerial imagery and remote sensing. Current research emphasizes improving accuracy and efficiency through novel loss functions (e.g., those based on Wasserstein distance or Kalman filtering), semi-supervised learning techniques to leverage unlabeled data, and the adaptation of transformer-based architectures like DETR for oriented bounding box regression. These advancements are crucial for applications requiring precise object localization in challenging scenarios, such as autonomous driving and environmental monitoring.