OBB (Oriented Bounding Box) object detection is an advanced form of object detection where the bounding boxes are rotated to align more accurately with the orientation of the objects, rather than just using standard axis-aligned rectangles. This technique is particularly useful when detecting objects that are not aligned with the horizontal or vertical axes, such as ships in satellite images, cars in aerial footage, or text in scanned documents. By representing objects with oriented bounding boxes, OBB detection provides a more precise localization, especially for elongated or rotated shapes, reducing background noise and overlapping issues.
OBB object detection requires predicting not only the object’s class and location but also its rotation angle. This adds complexity to the detection task, as the model must learn to understand and estimate object orientation. Specialized datasets (e.g., DOTA for aerial images) and adapted models (such as Rotated RetinaNet or R3Det) are often used for training and evaluation. Despite being more computationally intensive, OBB detection offers significant benefits in scenarios where orientation matters, making it essential for applications like remote sensing, robotics navigation, and industrial inspection.