Smooth GIoU Loss for Oriented Object Detection in Remote Sensing Images

Abstract

Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that spatial parameters are independent of one another. This independence is an ideal that is not achievable in practice. To avoid this problem, various kinds of IoU-based BBR losses have been widely used in OOD; however, their relationships with IoUs are approximately linear. Consequently, the gradient value, i.e., the learning intensity, cannot be dynamically adjusted with the IoU in these cases, which restricts the accuracy of object location. To handle this problem, a novel BBR loss, named smooth generalized intersection over union (GIoU) loss, is proposed. The contributions it makes include two aspects. First of all, smooth GIoU loss can employ more appropriate learning intensities in the different ranges of GIoU values to address the above problem and the design scheme of smooth GIoU loss can be generalized to other IoU-based BBR losses. Secondly, the existing computational scheme of GIoU loss can be modified to fit OOD. The ablation study of smooth GIoU loss validates the effectiveness of its design scheme. Comprehensive comparisons performed on two RSI datasets demonstrate that the proposed smooth GIoU loss is superior to other BBR losses adopted by existing OOD methods and can be generalized for various kinds of OOD methods. Furthermore, the core idea of smooth GIoU loss can be generalized to other IoU-based BBR losses.