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• W3207637322 endingPage "6996" @default.
• W3207637322 startingPage "6996" @default.
• W3207637322 abstract "Sky 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." @default.
• W3207637322 created "2021-10-25" @default.
• W3207637322 creator A5042555318 @default.
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• W3207637322 date "2021-10-21" @default.
• W3207637322 modified "2023-10-14" @default.
• W3207637322 title "Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers" @default.
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• W3207637322 doi "https://doi.org/10.3390/s21216996" @default.
• W3207637322 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8588092" @default.
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• W3207637322 hasPublicationYear "2021" @default.
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