Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN

doi:10.1109/TIM.2022.3145388

	Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN
	Fu, Yichen 1,2; Fan, Junfeng1,2 ; Xing, Shiyu 1,2; Wang, Zhe1,2 ; Jing, Fengshui1,2 ; Tan, Min1,2
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
	2022
卷号	71 页码:12
通讯作者	Jing, Fengshui(fengshui.jing@ia.ac.cn)
摘要	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.
关键词	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
关键词[WOS]	UNCERTAINTIES EVALUATION ; ALIGNMENT SYSTEM ; AIRCRAFT ; CALIBRATION ; COMPONENT
收录类别	SCI
语种	英语
资助项目	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]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000761251000025
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	多模态智能
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48070
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Jing, Fengshui
作者单位	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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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.