THE KFIOU LOSS FOR ROTATED OBJECT DETECTION

Abstract

Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this paper, we propose an effective approximate SkewIoU loss based on Gaussian modeling and Gaussian product, which mainly consists of two items. The first term is a scale-insensitive center point loss, which is used to quickly narrow the distance between the center points of the two bounding boxes. In the distance-independent second term, the product of the Gaussian distributions is adopted to inherently mimic the mechanism of SkewIoU by its definition, and show its alignment with the SkewIoU loss at trend-level within a certain distance (i.e. within 9 pixels). This is in contrast to recent Gaussian modeling based rotation detectors e.g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors. The resulting new loss called KFIoU loss is easier to implement and works better compared with exact SkewIoU loss, thanks to its full differentiability and ability to handle the non-overlapping cases. We further extend our technique to the 3-D case which also suffers from the same issues as 2-D. Extensive results on various datasets with different base detectors show the effectiveness of our approach.

1. INTRODUCTION

Rotated object detection is a relatively emerging but challenging area, due to the difficulties of locating the arbitrary-oriented objects and separating them effectively from the background, such as aerial images (Yang et al., 2018a; Ding et al., 2019; Yang et al., 2018b) , scene text (Jiang et al., 2017; Zhou et al., 2017) . Though considerable progresses have been recently made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution. The Skew Intersection over Union (SkewIoU) between large aspect ratio objects is sensitive to the deviations of the object positions. This causes the negative impact of the inconsistency between metric (dominated by SkewIoU) and regression loss (e.g. l n -norms), which is common in horizontal detection, and is further amplified in rotation detection. The red and orange arrows in Fig. 1 show the inconsistency between SkewIoU and Smooth L1 Loss. Specifically, when the angle deviation is fixed (red arrow), SkewIoU will decrease sharply as the aspect ratio increases, while the Smooth L1 loss is unchanged (mainly from the angle difference). Similarly, when SkewIoU does not change (orange arrow), Smooth L1 loss increases as the angle deviation increases.

funding

* Correspondence author is Junchi Yan who is also affiliated with Shanghai AI Laboratory. The work was partly done when the first author Xue Yang was an intern at Huawei Cloud. The work was also in part supported by NSFC (62222607), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102).

