Image- and Instance-Level Data Augmentation for Occluded Instance Segmentation

Instance segmentation is a fundamental computer vision task with widespread applications. Numerous novel methods have been proposed to address this task. However, limited data and occlusion are common issues that hinder the practical application of instance segmentation. In this paper, we address limited data issue by employing image-level data augmentation. Additionally, to address the occlusion issue, we propose Balanced Occlusion Aware Copy-Paste (BOACP), a method that can not only increase the number of instances in images but also balance occluded instances at the image level. This method can enhance the performance of model on occluded instances. For the model, we utilize the Hybrid Task Cascade (HTC) based on CBSwin-Base and CBFPN. Moreover, we conduct additional experiments to explore the Occlusion Metric (OM). Experimental results demonstrate the effectiveness of our proposed approach, and we achieve the first place in the first phase of DeepSportRadar Instance Segmentation Challenge in ACM MM-Sports 2023 Workshop.