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.
翻译：ACM MMSports 2022 Deep SportSportRadar Prime Discription Clubtion Creative Creative Creative Challenge 挑战的目标是解决包括球球场球员、教练和裁判在内的个别人类的分化问题。 这一挑战的主要特征是球员之间的分化程度高,数据的数量也相当有限。 为了解决这些问题,我们设计了一个强大的分解管道。 首先,我们为此任务采用了一个适当的数据增强战略,主要包括光度扭曲变异和影印式涂片战略,这可以产生更多的图像实例,分布范围更广。 其次,我们采用了一个强大的分解模型,以基于Swin-Base CBase CBNetV2主干线的混合任务卡塞探测器为基础,并将MuskioU头添加到HTCMask头,这样可以简单有效地改善分解特性的性能。 最后, SWA培训战略应用来进一步改进性能。 实验结果表明,拟议的管道可以在深SportRadar挑战上取得竞争性的结果, 0.68AP@0.50:0.95 关于挑战集的源码, 源码代码可在 https://giuth_yusment.
相关内容
微信扫码咨询专知VIP会员