Effective Rotate: Learning Rotation-robust Prototype for Aerial Object Detection

Aerial images often depict objects with arbitrary orientations, which pose challenges for conventional object detectors to detect and classify. To address this issue, rotation-equivariant Convolutional Neural Networks (CNNs) have been proposed to extract rotation-equivariant features. However, the orientation encoding in these networks is often unstable and noisy, deteriorating detection performance. In this paper, we first analyze the rotation-equivariant network. Then, we propose a Rotation-robust Prototype Generation (RPG) method, which consists of two parts, stabilization module and enhancement module. In stabilization module, we generate rotation-robust prototypes to increase the stability of cyclic shifts. In enhancement module, we use the obtained prototype to improve the response of the features to object semantics. The RPG method can be used as a plug-and-play module in both one-stage and two-stage detectors. With only 30 lines of code, we achieve an average 1% improvement on four challenging datasets, including DOTA-V1.5, DOTA-v1.0, DIOR-R, and HRSC2016.