A Simple Baseline with Placement Prior for Point-Supervised Oriented Object Detection

Oriented object detection for aerial images has attractedgrowing attention due to the increasing demand in extensive applica-tions. However, the oriented box annotation is relatively expensive, andthus point-supervised oriented object detection has emerged as a practi-cal paradigm. Owing to the lack of size and angle annotations, existingmethods leverage various complementary information, but usually sufferfrom extra training stages or multiplied computational overhead. In thispaper, we propose three simple yet effective loss terms, and set a simplebaseline with the placement prior. In particular, the placement prior in-dicates that objects in aerial images are placed in a context, rather thanexhibiting arbitrary distribution. Therefore, we propose the angle priorloss, considering that the angles of objects are usually aligned in a neigh-borhood. Furthermore, we propose the size prior loss, considering thatthe sizes of objects are usually proportional for each class in an image.Besides, we propose to derive oriented boxes from the Voronoi Water-shed regions without training, which can jointly regularize the estimatedangles and sizes. By employing only loss terms, our baseline is not onlysimple and easy to extend but also readily transferable to other methods.We conduct extensive experiments on representative UAV datasets andremote sensing datasets, which indicates the effectiveness of our method.