[1]JIN Shaowei,LIU Huaping,WANG Bowen,et al.Recognition of unknown materials in an open environment[J].CAAI Transactions on Intelligent Systems,2020,15(5):1020-1027.[doi:10.11992/tis.201903026]
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Recognition of unknown materials in an open environment

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 1020-1027 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2020-09-05
Title:
Recognition of unknown materials in an open environment
Author(s):
JIN Shaowei1; 2;  LIU Huaping3;  WANG Bowen1; 2;  SUN Fuchun3
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China;
3. State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China
Keywords:
open environment; tactile perception; sound data; distance measurement; support vector machine; k-nearest neighbor; material recognition; classification
CLC:
TP391
DOI:
10.11992/tis.201903026
Abstract:
Considering the problem of unknown object material recognition in an open environment, this paper proposes an object material recognition method framework that uses the Euclidean distance to distinguish unknown and known categories. Under this framework, a support vector machine is used to recognize object materials, and the classification effect is remarkable. This method uses the Euclidean distance in the distance metric to compare the thresholds. Objects whose average distances are less than the threshold are classified as materials of a known class; objects with distances greater than the threshold are classified as materials of an unknown class and use a support vector machine algorithm for re-learning recognition. Experiments are conducted on sound data in a haptic data set by the Technical University of Munich. Five distance measurement methods are compared, and finally, the Euclidean distance is selected. A comparison with the open set sparse representation classification method shows that the method proposed in this paper has certain advantages on the sound data set. A reasonable threshold is selected through experiments, and finally all object materials are recognized in an open environment. Experiments verify that the framework can solve the problem of object material recognition of tactile perception information in an open environment.
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