Approach to Object Hardness Prediction by Rubber Ball Hardness Prediction Using Capsule Network

Shota Shindo, Takaaki Goto, Kensei Tsuchida


A hardness is often used as an index to compare similar objects such as fruits or wood. To measure an object’s hardness, a hardness meter is required, and certain conditions must be met. The conditions are that the hardness meter is compatible with the object and must be close at hand. This research shows the possibility of measuring hardness without a hardness meter using a neural network. The method employs machine learning using a capsule network (CapsNet) of a neural network model. This research experimented using CapsNet with routing-by-agreement, CapsNet with expectation-maximization routing (EM routing) and the EM routing method with the addition of Tasks-Constrained Deep Convolutional Network (TCDCN). The four-layer CapsNet with EM routing implemented has achieved the state-of-the-art.  Multi-layered CapsNet with EM routing was a very effective method for regression analysis as well. And, CapsNet has higher discriminative power using EM-routing than routing-by-agreement.


Capsule network; Hardness; Regression analysis; Image process; Neural network

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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