[1]WANG Zhaoxin,XU Xinying,LIU Huaping,et al.Cascade broad learning for multi-modal material recognition[J].CAAI Transactions on Intelligent Systems,2020,15(4):787-794.[doi:10.11992/tis.201908021]
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Cascade broad learning for multi-modal material recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 787-794 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2020-07-05
Title:
Cascade broad learning for multi-modal material recognition
Author(s):
WANG Zhaoxin1;  XU Xinying1;  LIU Huaping2; 3;  SUN Fuchun2; 3
1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030600, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Technology and System, Tsinghua University, Beijing 100084, China
Keywords:
cascade structure; broad learning method; multi-modal fuse; material recognition; spectral data; homogeneous data; feature extraction; neural network
CLC:
TP391
DOI:
10.11992/tis.201908021
Abstract:
Material recognition plays a vital role in the interaction betwee-n the robot and the surrounding environment. The visual, tactile and auditory modalities can provide different properties of various materials. How to use signals of different modalities to complete the task of material identification quickly and efficiently is an urgent problem to be solved. Moreover, in practical applications, the data collected by the sensor is limited, so it cannot provide enough data for deep neural network for learning and training. To this end, this paper applies the cascade broad learning with good generalization performance to the material recognition task of small samples. Firstly, feature fusion of two sets of homogeneous multi-modal data is carried out, and then feature learning is carried out by using the broad learning of cascading feature nodes, The results show that compared with other methods, the method proposed in this paper reduces the training time and improves the classification performance while completing the material recognition tasks.
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