TOSO: Student’s-T Distribution Aided One-Stage Orientation Target Detection in Remote Sensing Images

In this paper, a robust Student's-T distribution aided One-Stage Orientation detector, namely TOSO, is proposed to address orientation target detection in remote sensing images. A one-stage keypoint based network architecture is used to avoid the complicated computation caused by rotation anchor boxes and two main contributions are proposed to enhance the performance. Firstly, a novel geometric transformation method is introduced to provide an orientation bounding box from its surrounding horizontal bounding box, so that the orientation angle is achieved by only regressing the geometric transformation parameters. Secondly, the Student's-t distribution is used as a joint distribution to associate the classification task with the regression task, which are represented as Gaussian and inverse Gamma distributions, respectively. Experiments on two popular remote sensing public datasets DOTA and HRSC2016 confirm the improvement from our proposed TOSO detector.