A Convolutional Neural Network for Soft Robot Images Classification
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Abstract
Description
In this work, a Convolutional Neural Network (CNN) is used to classify the images of soft
robotic actuators as bending, triangle, and muscle actuators. The classifier model is built
with a total 390 images of soft actuators comprising the soft actuators with 130 images for
bending, triangle, and muscle actuators, respectively. 70% of the images were used for
training, while 30% were used for validation. The developed CNN model achieved a loss of
7.63% and accuracy of 97.6% for the training data while a loss of 9.64% and accuracy of
85.71% was obtained on the validation data.
Keywords
QA75 Electronic computers. Computer science, TK Electrical engineering. Electronics Nuclear engineering