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

doi:10.1109/TIM.2022.3145388

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN
	Fu, Yichen 1,2; Fan, Junfeng 1,2; Xing, Shiyu 1,2; Wang, Zhe 1,2; Jing, Fengshui 1,2; Tan, Min 1,2
刊名	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
	2022
卷号	71 页码:12
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
ISSN号	0018-9456
DOI	10.1109/TIM.2022.3145388
通讯作者	Jing, Fengshui(fengshui.jing@ia.ac.cn)
英文摘要	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.
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]
WOS关键词	UNCERTAINTIES EVALUATION ; ALIGNMENT SYSTEM ; AIRCRAFT ; CALIBRATION ; COMPONENT
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000761251000025
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48069]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
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
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.