Sky and ground segmentation in the navigation visions of the planetary rovers

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dc.contributor.authorKuang, Boyu
dc.contributor.authorRana, Zeeshan A.
dc.contributor.authorZhao, Yifan
dc.date.accessioned2021-10-27T10:56:54Z
dc.date.available2021-10-27T10:56:54Z
dc.date.issued2021-10-21
dc.description.abstractSky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.en_UK
dc.identifier.citationKuang B, Rana Z, Zhao Y. (2021) Sky and ground segmentation in the navigation visions of the planetary rovers. Sensors, Volume 21, Issue 21, Article number 6996en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s21216996
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17209
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectsemantic segmentationen_UK
dc.subjectweak supervisionen_UK
dc.subjecttransfer learningen_UK
dc.subjectconservative annotation methoden_UK
dc.subjectvisual navigationen_UK
dc.subjectvisual sensoren_UK
dc.titleSky and ground segmentation in the navigation visions of the planetary roversen_UK
dc.typeArticleen_UK

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