A Convolutional Neural Network for Soft Robot Images Classification

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dc.creatorOguntosin, V., Akindele, Ayoola, Uyi, Aiyudubie
dc.date2020
dc.date.accessioned2025-04-15T12:32:54Z
dc.descriptionIn 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.
dc.formatapplication/pdf
dc.identifierhttp://eprints.covenantuniversity.edu.ng/18657/
dc.identifier.urihttps://repository.covenantuniversity.edu.ng/handle/123456789/49255
dc.languageen
dc.subjectQA75 Electronic computers. Computer science, TK Electrical engineering. Electronics Nuclear engineering
dc.titleA Convolutional Neural Network for Soft Robot Images Classification
dc.typeConference or Workshop Item

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