Oriented object detection typically adds an additional rotation angle to the regressed horizontal bounding box (HBB) for representing the oriented bounding box (OBB). However, existing oriented object detectors based on regression angles face inconsistency between metric and loss, boundary discontinuity or square-like problems. To solve the above problems, we propose an anchor-free oriented object detector named PRA-Det, which assigns the center region of the object to regress OBBs represented by the polar radius vectors. Specifically, the proposed PRA-Det introduces a diamond-shaped positive region of category-wise attention factor to assign positive sample points to regress polar radius vectors. PRA-Det regresses the polar radius vector of the edges from the assigned sample points as the regression target and suppresses the predicted low-quality polar radius vectors through the category-wise attention factor. The OBBs defined for different protocols are uniformly encoded by the polar radius encoding module into regression targets represented by polar radius vectors. Therefore, the regression target represented by the polar radius vector does not have angle parameters during training, thus solving the angle-sensitive boundary discontinuity and square-like problems. To optimize the predicted polar radius vector, we design a spatial geometry loss to improve the detection accuracy. Furthermore, in the inference stage, the center offset score of the polar radius vector is combined with the classification score as the confidence to alleviate the inconsistency between classification and regression. The extensive experiments on public benchmarks demonstrate that the PRA-Det is highly competitive with state-of-the-art oriented object detectors and outperforms other comparison methods.

The application of oriented object detection in the field of aerial images has gained substantial attention and made significant progress. However, most one-stage object detectors struggle to extract rotation-invariant features of oriented objects using ordinary convolutions. And the structure of two parallel vision subtasks can result in the inconsistency between the classification and regression. In this article, we propose a Task Integration based on a Rotated convolution kernel Network (TIR-Net) consisting of three modules: selective rotation of the kernel (SRK), regression feature refinement (RFR), and task integration (TI). Specifically, an SRK module enhances classification features by applying rotated convolution kernels selectively, introducing rotational invariance to the features. An RFR module places more emphasis on feature extraction of large aspect ratio objects to improve their perceptibility. A TI module integrates classification and regression features to alleviate the inconsistency between classification and regression. Experimental evaluations on the benchmarks for oriented object detection indicate that our method achieves excellent detection performance.