Improving Crowded Object Detection via Copy-Paste

• URL: http://arxiv.org/abs/2211.12110v1
• Date: Tue, 22 Nov 2022 09:25:15 GMT
• Title: Improving Crowded Object Detection via Copy-Paste
• Authors: Jiangfan Deng, Dewen Fan, Xiaosong Qiu, Feng Zhou
• Abstract summary: Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. We first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD)
• Score: 6.941267349187447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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