.png){kind=icon}
返回
Efficient and Small Network Using Multi-Trim Network Structure for Tactile Object Recognition on Embedded Systems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3014879.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robot sensing systems
Embedded systems
Object recognition
Computational efficiency
Sensitivity
Optical sensors
Image classification
convolutional neural network
network compression
embedded system
tactile sensor
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Novel Tactile-SIFT Descriptor for Object Shape Recognition用于物体形状识别的新型触觉-SIFT描述符.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Mechanism of carbonate mineralization induced by microbes: Taking Curvibacter lanceolatus strain HJ-1 as an exampleMicron
MicronIF0
PREAI
分享收藏Damage Detection and Level Classification of Roof Damage After Typhoon Faxai Based on Aerial Photo and Deep Learning基于航拍照片和深度学习的Faxai台风后顶板损伤检测与等级分类
OAAI
IF0
OAAI分享收藏Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏The power of obfuscation techniques in malicious JavaScript code: A measurement study恶意JavaScript代码中混淆技术的功能: 一项测量研究OAAI
OAAI分享收藏
查看更多内容