  The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation 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 strong instance segmentation pipeline. Firstly, we employed a proper
data augmentation strategy for this task mainly including photometric
distortion transform and copy-paste strategy, which can generate more image
instances with a wider distribution. Secondly, we employed a strong
segmentation model, Hybrid Task Cascade based detector on the Swin-Base based
CBNetV2 backbone, and we add MaskIoU head to HTCMaskHead that can simply and
effectively improve the performance of instance segmentation. Finally, the SWA
training strategy was applied to improve the performance further. Experimental
results demonstrate the proposed pipeline can achieve a competitive result on
the DeepSportRadar challenge, with 0.768AP@0.50:0.95 on the challenge set.
Source code is available at
https://github.com/YJingyu/Instanc_Segmentation_Pro.
