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In oriented object detection, current representations of oriented boundingboxes (OBBs) often suffer from boundary discontinuity problem. Methods ofdesigning continuous regression losses do not essentially solve this problem.Although Gaussian bounding box (GBB) representation avoids this problem,directly regressing GBB is susceptible to numerical instability. We proposelinear GBB (LGBB), a novel OBB representation. By linearly transforming theelements of GBB, LGBB avoids the boundary discontinuity problem and has highnumerical stability. In addition, existing convolution-based rotation-sensitivefeature extraction methods only have local receptive fields, resulting in slowfeature aggregation. We propose ring-shaped rotated convolution (RRC), whichadaptively rotates feature maps to arbitrary orientations to extractrotation-sensitive features under a ring-shaped receptive field, rapidlyaggregating features and contextual information. Experimental resultsdemonstrate that LGBB and RRC achieve state-of-the-art performance.Furthermore, integrating LGBB and RRC into various models effectively improvesdetection accuracy.

Linear Gaussian Bounding Box Representation and Ring-Shaped Rotated Convolution for Oriented Object Detection