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20 June 2023 Research on object classification based on visual-tactile fusion
Peng Zhang, Lu Bai, Dongri Shan
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Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 127152L (2023) https://doi.org/10.1117/12.2682381
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China
Abstract
As two modes of direct contact between robots and external environment, visual and tactile play a critical role in improving robot perception ability. In the real environment, it is difficult for the robot to achieve high accuracy when classifying objects only by a single mode (visual or tactile). In order to improve the classification accuracy of robots, a novel visual-tactile fusion method is proposed in this paper. Firstly, the ResNet18 is selected as the backbone network to extract visual features. To improve the accuracy of object localization and recognition in the visual network, the Position-Channel Attention Mechanism (PCAM) block is added after conv3 and conv4 of ResNet18. Then, the four-layer one-dimensional convolutional neural network is used to extract tactile features, and the extracted tactile features are fused with visual features at the feature layer. Finally, the experimental results demonstrate that compared with the existing methods, on the self-made dataset VHAC-52, the proposed method has improved the AUC and ACC by 1.60% and 1.47%, respectively.
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Peng Zhang, Lu Bai, and Dongri Shan "Research on object classification based on visual-tactile fusion", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 127152L (20 June 2023); https://doi.org/10.1117/12.2682381
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KEYWORDS
Feature extraction
Feature fusion
Robots
Object recognition
Convolutional neural networks
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