论文标题
ASAP-NMS：使用具有空间意识的先验加速非最大抑制
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
论文作者
论文摘要
非最大抑制（或贪婪-NMS）的广泛采用的顺序变体是对象检测管道的关键模块。不幸的是，对于两个/多阶段探测器的区域建议阶段，NMS由于其顺序性质而成为潜伏期瓶颈。在本文中，我们仔细介绍了贪婪-NMS的迭代，发现在比较已经很遥远且相互抑制的机会很小的可能性时，浪费了很大一部分计算。我们仅比较附近锚生成的建议来解决这个问题。锚定晶格的翻译不变属性可生成一张查找表，在NMS期间，该表提供了有效访问附近建议的访问权限。这导致了一种加速的NMS算法，该算法利用了空间意识的先验或ASAP-NMS，并在CPU上将NMS步骤的延迟从13.6ms提高到1.2 ms，而无需牺牲Coco和VOC数据集上尚未达到的两阶段探测器的准确性。重要的是，ASAP-NMS对图像分辨率不可知，并且可以用作推理过程中简单的倒入模块。仅在运行时使用ASAP-NMS，我们使用V100 GPU在可可数据集上获得44.2 \％@25Hz的地图。
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of two/multi-stage detectors, NMS is turning out to be a latency bottleneck due to its sequential nature. In this article, we carefully profile Greedy-NMS iterations to find that a major chunk of computation is wasted in comparing proposals that are already far-away and have a small chance of suppressing each other. We address this issue by comparing only those proposals that are generated from nearby anchors. The translation-invariant property of the anchor lattice affords generation of a lookup table, which provides an efficient access to nearby proposals, during NMS. This leads to an Accelerated NMS algorithm which leverages Spatially Aware Priors, or ASAP-NMS, and improves the latency of the NMS step from 13.6ms to 1.2 ms on a CPU without sacrificing the accuracy of a state-of-the-art two-stage detector on COCO and VOC datasets. Importantly, ASAP-NMS is agnostic to image resolution and can be used as a simple drop-in module during inference. Using ASAP-NMS at run-time only, we obtain an mAP of 44.2\%@25Hz on the COCO dataset with a V100 GPU.
