Multimode fusion perception for transparent glass recognition
ISSN: 0143-991X
Article publication date: 18 March 2022
Issue publication date: 1 June 2022
Abstract
Purpose
The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.
Design/methodology/approach
A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.
Findings
The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.
Originality/value
Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.
Keywords
Acknowledgements
This paper forms part of a special section “Dexterous Manipulation”, guest edited by Bin Fang, Qiang Li, Fei Chen and Weiwei Wan.
This work was supported by the National Natural Science Foundation of China with Grant No. 62173197 and Beijing Science Technology Project with Grant No. Z191100008019008, the State Key Laboratory of Automotive Safety and Energy No. KF2006, Natural Science Foundation of Anhui Province No. 2108085MF224.
Citation
Zhang, S., Shan, J., Sun, F., Fang, B. and Yang, Y. (2022), "Multimode fusion perception for transparent glass recognition", Industrial Robot, Vol. 49 No. 4, pp. 625-633. https://doi.org/10.1108/IR-12-2021-0295
Publisher
:Emerald Publishing Limited
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