Pseudo-IoU:改进无锚物体探测中的标签分配 (Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection)

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric. In this paper, we present \textbf{Pseudo-Intersection-over-Union~(Pseudo-IoU)}: a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://github.com/SHI-Labs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

翻译：目前,无锚物体探测器非常简单、有效,但缺乏准确的标签分配方法,限制了它们与传统的基于锚的模型竞争的潜力,而传统的基于锚的模型则以基于跨交-Ulion~(IoU)的精心设计的任务分配方法支持。在本文中,我们提出\ textbf{Pseudo-Intersedu-Intersection-over-Uniion~(Pseeudo-IoU)}:一个简单的指标,将更标准化和准确的任务分配规则引入无锚物体探测框架,而不增加任何计算成本或额外的培训和测试参数,从而有可能通过利用以前在基于锚的方法中应用的有效任务分配规则下的良好质量培训样本来进一步改进无锚物体探测。通过将Pseudo-IoUloU的矩阵纳入端至端单阶段无锚物体探测框架,我们观察到在普通物体探测基准(如PASAL VOC和MCCO)上的性能持续改进。我们的方法(单一模型和单一尺度)还将达到与最近的州-艺术-固定-PI-Peb-freal-to-com-to-to-to-to-to-to-to-to-to-to-to-to-to-st-to-to-tole-st-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-tob-st-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-st-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-tob-to-to-to-to-to-to-to-to-to-to-to-to-tob-to-to-to-to-to-to-to-to-s-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-s-to-to-to-to-to-to-to-to-to-to-s-s-s-s-s-