  Crowdedness caused by overlapping among similar objects is a ubiquitous
challenge in the field of 2D visual object detection. In this paper, we first
underline two main effects of the crowdedness issue: 1) IoU-confidence
correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we
explore a pathway of cracking these nuts from the perspective of data
augmentation. Primarily, a particular copy-paste scheme is proposed towards
making crowded scenes. Based on this operation, we first design a "consensus
learning" method to further resist the ICD problem and then find out the
pasting process naturally reveals a pseudo "depth" of object in the scene,
which can be potentially used for alleviating CDD dilemma. Both methods are
derived from magical using of the copy-pasting without extra cost for
hand-labeling. Experiments show that our approach can easily improve the
state-of-the-art detector in typical crowded detection task by more than 2%
without any bells and whistles. Moreover, this work can outperform existing
data augmentation strategies in crowded scenario.
