Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation
A novel interactive maintenance method for space station in-orbit devices using scene semantic segmentation technology is proposed. First, a wearable and handheld system is designed to capture images from the astronaut in the space station’s front view scene and play these images on a handheld termi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/542 |
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| author | Haoting Liu Chuanxin Liao Xikang Li Zhen Tian Mengmeng Wang Haiguang Li Xiaofei Lu Zhenhui Guo Qing Li |
| author_facet | Haoting Liu Chuanxin Liao Xikang Li Zhen Tian Mengmeng Wang Haiguang Li Xiaofei Lu Zhenhui Guo Qing Li |
| author_sort | Haoting Liu |
| collection | DOAJ |
| description | A novel interactive maintenance method for space station in-orbit devices using scene semantic segmentation technology is proposed. First, a wearable and handheld system is designed to capture images from the astronaut in the space station’s front view scene and play these images on a handheld terminal in real-time. Second, the proposed system quantitatively evaluates the environmental lighting condition in the scene by calculating image quality evaluation parameters. If the lighting condition is not proper, a prompt message will be given to the astronaut to remind him or her to adjust the environment illumination. Third, our system adopts an improved DeepLabV3+ network for semantic segmentation of these astronauts’ forward view scene images. Regarding the improved network, the original backbone network is replaced with a lightweight convolutional neural network, i.e., the MobileNetV2, with a smaller model scale and computational complexity. The convolutional block attention module (CBAM) is introduced to improve the network’s feature perception ability. The atrous spatial pyramid pooling (ASPP) module is also considered to enable an accurate calculation of encoding multi-scale information. Extensive simulation experiment results indicate that the accuracy, precision, and average intersection over the union of the proposed algorithm can be better than 95.0%, 96.0%, and 89.0%, respectively. And the ground application experiments have also shown that our proposed technique can effectively shorten the working time of the system user. |
| format | Article |
| id | doaj-art-b44e393fc48940d28f06e0d7c957431a |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-b44e393fc48940d28f06e0d7c957431a2025-08-20T02:24:00ZengMDPI AGAerospace2226-43102025-06-0112654210.3390/aerospace12060542Interactive Maintenance of Space Station Devices Using Scene Semantic SegmentationHaoting Liu0Chuanxin Liao1Xikang Li2Zhen Tian3Mengmeng Wang4Haiguang Li5Xiaofei Lu6Zhenhui Guo7Qing Li8Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaJiuquan Satellite Launch Center, Jiuquan 732750, ChinaJiuquan Satellite Launch Center, Jiuquan 732750, ChinaJiuquan Satellite Launch Center, Jiuquan 732750, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaA novel interactive maintenance method for space station in-orbit devices using scene semantic segmentation technology is proposed. First, a wearable and handheld system is designed to capture images from the astronaut in the space station’s front view scene and play these images on a handheld terminal in real-time. Second, the proposed system quantitatively evaluates the environmental lighting condition in the scene by calculating image quality evaluation parameters. If the lighting condition is not proper, a prompt message will be given to the astronaut to remind him or her to adjust the environment illumination. Third, our system adopts an improved DeepLabV3+ network for semantic segmentation of these astronauts’ forward view scene images. Regarding the improved network, the original backbone network is replaced with a lightweight convolutional neural network, i.e., the MobileNetV2, with a smaller model scale and computational complexity. The convolutional block attention module (CBAM) is introduced to improve the network’s feature perception ability. The atrous spatial pyramid pooling (ASPP) module is also considered to enable an accurate calculation of encoding multi-scale information. Extensive simulation experiment results indicate that the accuracy, precision, and average intersection over the union of the proposed algorithm can be better than 95.0%, 96.0%, and 89.0%, respectively. And the ground application experiments have also shown that our proposed technique can effectively shorten the working time of the system user.https://www.mdpi.com/2226-4310/12/6/542space station maintenancesemantic segmentationDeepLabV3+lighting perceptioninteractive operation |
| spellingShingle | Haoting Liu Chuanxin Liao Xikang Li Zhen Tian Mengmeng Wang Haiguang Li Xiaofei Lu Zhenhui Guo Qing Li Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation Aerospace space station maintenance semantic segmentation DeepLabV3+ lighting perception interactive operation |
| title | Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation |
| title_full | Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation |
| title_fullStr | Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation |
| title_full_unstemmed | Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation |
| title_short | Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation |
| title_sort | interactive maintenance of space station devices using scene semantic segmentation |
| topic | space station maintenance semantic segmentation DeepLabV3+ lighting perception interactive operation |
| url | https://www.mdpi.com/2226-4310/12/6/542 |
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