Recently, impressive improvements have been achieved in general object detection. However, tiny object detection remains a very challenging problem since tiny objects only occupy a few pixels. Consequently, the label assignment strategies used in general object detectors are not suitable for tiny object detection, because these algorithms tend to assign few or even no positive samples for tiny objects. In this article, we propose a simple yet effective Gaussian assignment (GA) strategy to solve this problem. Specifically, we first model the bounding boxes as 2-D Gaussian distributions and then encode training samples with a threshold. This strategy can assign more high-quality positive samples for tiny objects and adjust the weight of positive samples to balance the contribution from different-size objects. Extensive experiments on four tiny object detection datasets show that the proposed strategy significantly and consistently improves the performance of single-stage tiny object detectors. In particular, with our strategy, we bridge the performance gap between single-stage and state-of-the-art multistage detectors on the AI-TOD dataset (24.2% versus 24.8% in mAP) while maintaining the inference speed. The code is available at https://github.com/zf020114/GaussianAssignment .

中文翻译：

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标签分配很重要：微小物体检测的高斯分配策略


最近，在一般目标检测方面取得了令人印象深刻的改进。然而，微小物体检测仍然是一个非常具有挑战性的问题，因为微小物体只占据几个像素。因此，一般物体检测器中使用的标签分配策略不适合微小物体检测，因为这些算法往往为微小物体分配很少甚至没有正样本。在本文中，我们提出了一种简单而有效的高斯分配（GA）策略来解决这个问题。具体来说，我们首先将边界框建模为二维高斯分布，然后使用阈值对训练样本进行编码。该策略可以为微小物体分配更多高质量的正样本，并调整正样本的权重以平衡不同大小物体的贡献。对四个微小物体检测数据集的广泛实验表明，所提出的策略显着且持续地提高了单级微小物体检测器的性能。特别是，通过我们的策略，我们缩小了 AI-TOD 数据集上单级和最先进的多级探测器之间的性能差距（mAP 为 24.2% 对比 24.8%），同时保持推理速度。该代码可在https://github.com/zf020114/GaussianAssignment 。