Bounding box regression is one of the important steps of object detection.
However, rotation detectors often involve a more complicated loss based on
SkewIoU which is unfriendly to gradient-based training. Most of the existing
loss functions for rotated object detection calculate the difference between
two bounding boxes only focus on the deviation of area or each points distance
(e.g., $\mathcal{L}_{Smooth-\ell 1}$, $\mathcal{L}_{RotatedIoU}$ and
$\mathcal{L}_{PIoU}$). The calculation process of some loss functions is
extremely complex (e.g. $\mathcal{L}_{KFIoU}$). In order to improve the
efficiency and accuracy of bounding box regression for rotated object
detection, we proposed a novel metric for arbitrary shapes comparison based on
minimum points distance, which takes most of the factors from existing loss
functions for rotated object detection into account, i.e., the overlap or
nonoverlapping area, the central points distance and the rotation angle. We
also proposed a loss function called $\mathcal{L}_{FPDIoU}$ based on four
points distance for accurate bounding box regression focusing on faster and
high quality anchor boxes. In the experiments, $FPDIoU$ loss has been applied
to state-of-the-art rotated object detection (e.g., RTMDET, H2RBox) models
training with three popular benchmarks of rotated object detection including
DOTA, DIOR, HRSC2016 and two benchmarks of arbitrary orientation scene text
detection including ICDAR 2017 RRC-MLT and ICDAR 2019 RRC-MLT, which achieves
better performance than existing loss functions.

提出了一种基于点距离的任意形状比较的新型指标，以提高旋转对象检测中边界框回归的效率和准确性，并提出了一种基于四点距离的准确边界框回归损失函数 FPDIoU，通过三个旋转对象检测基准测试和两个任意方向场景文本检测基准测试的实验证明了其优于现有损失函数的性能。

FPDIoU Loss: 旋转目标检测中高效边界框回归的损失函数

FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of  Rotated Object Detection

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. In contrast, 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 public datasets (2-D/3-D, aerial/text/face images) with different base
detectors show the effectiveness of our approach.

本文提出了一种基于高斯建模和高斯乘积的有效近似 SkewIoU 损失，称为 KFIoU 损失，该损失易于实现并与基本检测器一起工作得更好，我们进一步将我们的技术扩展到 3D 案例。