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학술대회 Hidden Enemy Visualization using Fast Panoptic Segmentation on Battlefields
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저자
손진희, 이소연
발행일
202101
출처
International Conference on Big Data and Smart Computing (BigComp) 2021, pp.291-294
DOI
https://dx.doi.org/10.1109/BigComp51126.2021.00061
협약과제
19JR1700, 비정형 환경에서의 개별 전투원 시각 공간 인지증강 기술 연구, 이소연
초록
Most of enemies in battlefields are not visible due to cover and concealment, and it yields fear and a weakened combat power for allies. In order to overcome it, we present a new approach for visualizing hidden enemies in battlefields to enhance cognition ability and survival for soldiers. Our method is composed of two separate sub-task networks. One is an efficient real-time panoptic segmentation network based on YOLACT [1] to find hidden enemies as well as to understand scenes from the viewpoint of soldiers. The other is an image completion network to reconstruct occluded parts of enemies which is guided by the panoptic segmentation networks. Our experiments on the Cityscapes benchmarks show that the proposed panoptic segmentation network achieves almost realtime speed without significant performance drops. We also demonstrate qualitative results of our segmentation-guided image completion method successfully on a dataset constructed from images of the Battlefield4 game.
키워드
Battlefield, Deep Learning, Image Completion, Military, Panoptic Segmentation
KSP 제안 키워드
Completion method, Enhance Cognition, Image completion, New approach, Real-Time, deep learning(DL)