Ellipse IoU Loss: Better Learning for Rotated Bounding Box Regression

Rotated object detection is an important research content in the field of remote-sensing images. However, in the rotated object detection, the inconsistency between the loss function and the final detection metric has become an important factor restricting the improvement of detection accuracy. So, in this letter, an ellipse intersection over union (IoU) loss (EPIoU loss) is proposed to solve these problems. The EPIoU loss uses IoU between the bounding boxes’ inscribed ellipses, which is approximate to the original bounding box IoU. This loss function can jointly optimize the prediction box parameters and promote the model to locate the object better. Compared to the complex intersection of rotated rectangles, the intersection calculation of two rotated ellipses is simple. A unified and differentiable process is also designed to calculate EPIoU, which avoids the complexity of the original bounding box IoU calculation. The experiments on DOTA, DIOR, and HRSC datasets verify that the proposed loss function can effectively improve the accuracy of the model.