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.
翻译:与发达的横向物体探测区不同,前者是计算机友好型IOU基于IOU的损失很容易被采纳,而且与探测指标相适应。相比之下,轮换探测器往往涉及基于SkewIoU的更加复杂的损失,而SkewIoU对梯度培训不友好。在本文中,我们建议基于高山模型和高斯产品,以主要由两个项目组成,有效地估算SkewIoU的损失。第一个术语是规模不敏感的中心点损失,用于迅速缩小两个捆绑箱中心点之间的距离。在第二个远程独立的术语中,高斯分布的产产品往往涉及基于SkewIOU机制的复杂损失。在本定义中,我们建议基于高斯建模和高斯产品,主要由两个项目组成。这与最近的高斯建模基于轮换的测点测距方法(例如:GWD损失和KLD损失,其中涉及人端的距离测量,这需要额外的超离谱2号基数的计算结果,其基值与SKFI的数据在一定距离上与趋势的测算结果之间有差异(即我们不同的测算)相比,这与我们不同的测算为不同的测算。