Dual Data Augmentation Method for Data-Deficient and Occluded Instance Segmentation
Bo Yan, Yadong Li, Xingran Zhao, Hongbin Wang · 2022
Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data and occlusion are common problems in practical application. DeepSportRadar Instance Segmentation challenge has focused on these problems. The goal of DeepSportRadar challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court. And the main characteristics of this challenge are there is a high level of occlusions between players and the amount of data is quite limited. In order to address these problems, we designed a Dual Data Augmentation(DDA) method including an offline data augmentation(ODA) strategy to tackle the data-deficient problem, and an online specific copy-paste(OS-CP) strategy to address the occlusion issue. We demonstrate the applicability proposed method on DeepSportRadar Instance Segmentation challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Large-based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the DeepSportRadar challenge, with [email protected]:0.95 on the challenge set.