TIR-Net: Task Integration Based on Rotated Convolution Kernel for Oriented Object Detection in Aerial Images

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.