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pro vyhledávání: '"Xiang, Kaite"'
Semantic Segmentation (SS) is promising for outdoor scene perception in safety-critical applications like autonomous vehicles, assisted navigation and so on. However, traditional SS is primarily based on RGB images, which limits the reliability of SS
Externí odkaz:
http://arxiv.org/abs/2011.13313
Semantic segmentation is a critical method in the field of autonomous driving. When performing semantic image segmentation, a wider field of view (FoV) helps to obtain more information about the surrounding environment, making automatic driving safer
Externí odkaz:
http://arxiv.org/abs/2002.03736
Semantically interpreting the traffic scene is crucial for autonomous transportation and robotics systems. However, state-of-the-art semantic segmentation pipelines are dominantly designed to work with pinhole cameras and train with narrow Field-of-V
Externí odkaz:
http://arxiv.org/abs/1909.07721
Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination conditions. However, in face of adverse conditions such as the nighttime, semantic segmentation lose
Externí odkaz:
http://arxiv.org/abs/1908.05868
A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized Network
Semantic Segmentation (SS) is the task to assign a semantic label to each pixel of the observed images, which is of crucial significance for autonomous vehicles, navigation assistance systems for the visually impaired, and augmented reality devices.
Externí odkaz:
http://arxiv.org/abs/1907.11394
Semantic Segmentation (SS) is a task to assign semantic label to each pixel of the images, which is of immense significance for autonomous vehicles, robotics and assisted navigation of vulnerable road users. It is obvious that in different applicatio
Externí odkaz:
http://arxiv.org/abs/1907.11066
Akademický článek
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Publikováno v:
Proceedings of SPIE; September 2019, Vol. 11169 Issue: 1 p111690A-111690A-13, 11057324p
A comparative study of high-recall real-time semantic segmentation based on swift factorized network
Publikováno v:
Proceedings of SPIE; September 2019, Vol. 11169 Issue: 1 p111690C-111690C-14, 11057325p
Autor:
Stein, Karin U., Schleijpen, Ric, Shen, Jiafeng, Wang, Kaiwei, Yang, Kailun, Xiang, Kaite, Fei, Lei, Hu, Xinxin, Li, Huabing, Chen, Hao
Publikováno v:
Proceedings of SPIE; 8/9/2019, Vol. 11158, p1115807-1115807, 1p