A Novel Bilinear Feature and Multi-Layer Fused Convolutional Neural Network for Tactile Shape Recognition

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ID: 268054
2020
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Abstract
Convolutional neural networks (CNNs) can automatically learn features from pressure information, and some studies have applied CNNs for tactile shape recognition. However, the limited density of the sensor and its flexibility requirement lead the obtained tactile images to have a low-resolution and blurred. To address this issue, we propose a bilinear feature and multi-layer fused convolutional neural network (BMF-CNN). The bilinear calculation of the feature improves the feature extraction capability of the network. Meanwhile, the multi-layer fusion strategy exploits the complementarity of different layers to enhance the feature utilization efficiency. To validate the proposed method, a 26 class letter-shape tactile image dataset with complex edges was constructed. The BMF-CNN model achieved a 98.64% average accuracy of tactile shape. The results show that BMF-CNN can deal with tactile shapes more effectively than traditional CNN and artificial feature methods.
Reference Key
chu2020sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jie Chu;Jueping Cai;He Song;Yuxin Zhang;Linyu Wei;Chu, Jie;Cai, Jueping;Song, He;Zhang, Yuxin;Wei, Linyu;
Journal sensors
Year 2020
DOI
10.3390/s20205822
URL
https://doi.org/10.3390/s20205822
Keywords
Convolutional neural network tactile shape bilinear feature multi-layer fusion

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