Efficient and Small Network using Multi-Trim Network Structure for Tactile Object Recognition on Embedded Systems
Tactile object recognition (TOR) is critical in robot perception. However, as an embedded system, a robot brain has a fixed resource budget and is unsuitable for modern convolutional neural networks (CNNs). To bridge this gap, we present a simple network-compression approach that improves the accura...
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Published in | IEEE access Vol. 8; p. 1 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Tactile object recognition (TOR) is critical in robot perception. However, as an embedded system, a robot brain has a fixed resource budget and is unsuitable for modern convolutional neural networks (CNNs). To bridge this gap, we present a simple network-compression approach that improves the accuracy-latency trade-off of the network. The multi-trim network structure (MTNS) is a robust combination of network compression (NC) techniques providing a lightweight network with no performance drop. Furthermore, as an optical tactile sensor, we present a random-dot sensor that obtains rich information with a single touch, thus avoiding modality fusion. The random-dot sensor captures the object shapes and inputs them to TOR. In an experimental evaluation, we compare the performances of the proposed MTNS approach with those of CNN filter pruning, the network quantization technique, an adaptive mixture of low-rank factorizations, and knowledge distillation. The MTNS better resolved the accuracy-latency trade-off in tactile object recognition than the modern NC methods. By combining the random-dot sensor and MTNS approach, TOR enhances the accuracy and processing time performances. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2020.3014879 |