In this work, we propose a panoptic segmentation model that integrates bottom-up and top-down methods. Our framework is designed to guarantee both the performance and the inference speed. We also focus on improving the quality of semantic and instance masks. The proposed auxiliary task with the silhouette-based enhanced features can help the model improve the prediction quality of mask contours. Additionally, we introduce a new mask quality score intending to solve the occlusion problem. The model has less chance of ignoring small objects, which often have lower confidence scores than larger objects behind them. The results show that the proposed mask quality score can better distinguish the priority of objects when the occlusion occurs. We demonstrate the results of our work on two datasets: the COCO dataset and the CityScapes dataset. Via our approach, we obtained competitive results with fast inference time.
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