Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the aforementioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (e.g. +3.03% mAP on Rotated RetinaNet and +4.16% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP.

翻译：旋转对象检测旨在以任意方向的图像来识别和定位对象。 在此情况下, 对象的方向方向在不同的图像中差异很大, 而图像中有多个对象的多重方向。 这种内在特征使得标准主干网难以获取这些任意定向对象的高质量特征。 在本文中, 我们展示了适应性旋转进化( ARC) 模块, 以应对上述挑战。 在我们的 ARC 模块中, 变动内核会以适应的方式旋转, 以在不同图像中提取不同方向的物体特征, 并引入一个高效的有条件计算机制, 以适应图像中对象的巨大方向变异。 两种设计在旋转对象探测问题中无缝地运作。 此外, ARC 可以方便地作为各种视觉主干网的插和播放模块, 提高它们准确检测定向对象的能力。 在常用的基准( DOTA和 HRSC2016) 实验显示, 在主干网中安装了我们提议的 ARC 模块, 多个受欢迎的对象探测器的性能显著改进( 例如, +3.03 % mAP 关于旋转RetinNet 和 O+4.16% 的拟议DA- Rat- a a a 。 在高竞争状态上, 。</s>