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。