Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection

Detecting oriented and densely packed objects is a challenging problem considering that the receptive field intersection between objects causes spatial feature aliasing. In this paper, we propose a convex-hull feature adaptation (CFA) approach, with the aim to configure convolutional features in accordance with irregular object layouts. CFA roots in the convex-hull feature representation, which defines a set of dynamically sampled feature points guided by the convex intersection over union (CIoU) to bound object extent. CFA pursues optimal feature assignment by constructing convex-hull sets and iteratively splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA defines a systematic way to adapt convolutional features on regular grids to objects of irregular shapes. Experiments on DOTA and SKU110K-R datasets show that CFA achieved new state-of-the-art performance for detecting oriented and densely packed objects. CFA also sets a solid baseline for convex polygon prediction on the MS COCO dataset defined for general object detection. Code is available at github.com/SDL-GuoZonghao/BeyondBoundingBox.