Paper Info
Title
Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network
Abstract
In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength level of objects' elasticity and hardness attributes to make up for the lack of relevant data, and proposes a multi-scale convolutional neural network to identify the strength level of object attributes. The network recognizes the different attributes and identifies differences in the strength level of the same object attributes by fusing the original features, i.e., the single-channel features and multi-channel features of the data. A variety of evaluation methods were used for comparison with multiple models in terms of strength levels of elasticity and hardness. The results show that our network has a more significant effect in accuracy. In the prediction results of the positive examples in the predicted value, the true value has a higher proportion of positive examples, that is, the precision is better. The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network's ability to recognize each strength level is more stable.
Year
DOI
Venue
2022
10.3390/s22051908
SENSORS
Keywords
DocType
Volume
robot tactile, convolution neural network, attribute strength level, robot operating system
Journal
22
Issue
ISSN
Citations 
5
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Peng Zhang116.78
Guoqi Yu200.34
Dongri Shan300.34
Zhenxue Chen47411.60
Xiaofang Wang5367.83