.png){kind=icon}
返回
Surface Material Classification Based on Unbalanced Visual and Haptic Data: A Double-DQN Method
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2023.3244802.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The article focuses on surface material classification with unbalanced visual and haptic data, which is important in teleoperation and robotic recognition. For this problem, existent classification methods inevitably suffer from performance degradation, as they tend to emphasize the major classes and ignore the minor ones. To overcome such an issue, we address this classification problem by the double deep $Q$ -learning network (DDQN) method which not only offers strong representation ability but also avoids overestimation. Specifically, we first transform haptic accelerations to their spectrograms by short-time Fourier transform (STFT), and, respectively, feed visual images and haptic spectrograms to pretrained ResNet50 for extracting low-dimensional feature vectors. Then, the hybrid visual-haptic feature vectors are fed into DDQN as a sequence of states. With respect to each state, DDQN assigns it with an estimated class label. By comparing with the true label, DDQN earns a reward that is dependent on the imbalance ratio of the class. Through maximization of the cumulative rewards, DDQN enhances its classification performance. For the purposes of validation, numerical evaluations are carried out on the TUM69, LMT108, and HaTT datasets. The results show that DDQN outperforms the existent methods in both classification performance and computational complexity.
Keyword:
Haptic interfaces
Visualization
Feature extraction
Residual neural networks
Spectrogram
Deep learning
Convolutional neural networks
Double deep Q-learning network (DDQN)
hybrid visual-haptic data
residual network
surface material classification
unbalanced measurement
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Kinetics and mechanism of solid-state reaction between bismuth(III) oxide and molybdenum(VI) oxide
PREAI
分享收藏Human-Exploratory-Procedure-Based Hybrid Measurement Fusion for Material Recognition基于人类探索过程的混合测量融合用于材料识别
PREAI
分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容