We show a simple NMS-free, end-to-end object detection framework, of which
the network is a minimal modification to a one-stage object detector such as
the FCOS detection model [Tian et al. 2019]. We attain on par or even improved
detection accuracy compared with the original one-stage detector. It performs
detection at almost the same inference speed, while being even simpler in that
now the post-processing NMS (non-maximum suppression) is eliminated during
inference. If the network is capable of identifying only one positive sample
for prediction for each ground-truth object instance in an image, then NMS
would become unnecessary. This is made possible by attaching a compact PSS head
for automatic selection of the single positive sample for each instance (see
Fig. 1). As the learning objective involves both one-to-many and one-to-one
label assignments, there is a conflict in the labels of some training examples,
making the learning challenging. We show that by employing a stop-gradient
operation, we can successfully tackle this issue and train the detector. On the
COCO dataset, our simple design achieves superior performance compared to both
the FCOS baseline detector with NMS post-processing and the recent end-to-end
NMS-free detectors. Our extensive ablation studies justify the rationale of the
design choices.

本文介绍了一种基于 FCOS 检测模型的 NMS-free 端到端的目标检测框架，使用紧凑的 PSS head 实现单个目标实例的自动选择并消除后处理的 NMS，通过停梯度操作成功解决了一对多和一对一标签分配之间的冲突问题，实现了对 COCO 数据集的优越性能。