| Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN | |
Fu, Yichen1,2; Fan, Junfeng1,2 ; Xing, Shiyu1,2; Wang, Zhe1,2; Jing, Fengshui1,2; Tan, Min1,2
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| Source Publication | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| ISSN | 0018-9456 |
| 2022 | |
| Volume | 71Pages:12 |
| Corresponding Author | Jing, Fengshui(fengshui.jing@ia.ac.cn) |
| Abstract | Cabin pose measurement is one of the key procedures in the assembly and docking process of large cabins, which provides important feedback information for the subsequent docking control system. As the basis of cabin pose measurement, the accuracy and robustness of cabin assembly image segmentation are particularly important. However, traditional image segmentation method based on RGB sensor is extremely susceptible to interference from the external environment, which greatly weakens the recognition effect. In this article, an image segmentation method of cabin assembly scene based on improved red-green-blue-depth (RGB-D) Mask R-CNN is proposed, and its network structure is designed to be able to specifically process four-channel images. The method can accurately extract the corresponding area of the cabin under complex and severe environmental disturbances, with high robustness and generalization capability. Meanwhile, the excellence of deep learning segmentation algorithms with depth channel information input is highlighted. In experiments, improved classic segmentation network U-Net, SegNet, pyramid scene parsing network (PSPNet), and Deeplab-v3 based on RGB-D were constructed as control, and these models were tested and evaluated on the enhanced test sets to verify their segmentation accuracy and robustness performance. Comparing experiments fully demonstrate the superiority of the segmentation network model of RGB-D four-channel input over RGB input. At the same time, vision system using the proposed Mask R-CNN algorithm based on RGB-D has the best cabin segmentation accuracy, robustness, and generalization capability, which has practical significance for industrial applications. |
| Keyword | Image segmentation Robustness Production Position measurement Feature extraction Deep learning Adaptation models Cabin docking cabin pose measurement deep neural network (DNN) red-green-blue-depth (RGB-D) image segmentation RGB-D sensor |
| DOI | 10.1109/TIM.2022.3145388 |
| WOS Keyword | UNCERTAINTIES EVALUATION ; ALIGNMENT SYSTEM ; AIRCRAFT ; CALIBRATION ; COMPONENT |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[U1813208] ; National Natural Science Foundation of China[62003341] ; National Natural Science Foundation of China[62173327] ; National Natural Science Foundation of China[61903362] ; National Key Research and Development Program of China[2019YFB1312703] |
| Funding Organization | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| WOS Research Area | Engineering ; Instruments & Instrumentation |
| WOS Subject | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| WOS ID | WOS:000761251000025 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 多模态智能 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/48070 |
| Collection | 复杂系统管理与控制国家重点实验室_先进机器人 |
| Corresponding Author | Jing, Fengshui |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Fu, Yichen,Fan, Junfeng,Xing, Shiyu,et al. Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:12. |
| APA | Fu, Yichen,Fan, Junfeng,Xing, Shiyu,Wang, Zhe,Jing, Fengshui,&Tan, Min.(2022).Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,12. |
| MLA | Fu, Yichen,et al."Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):12. |
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