Oguntosin, V. and Akindele, Ayoola and Uyi, Aiyudubie (2020) A Convolutional Neural Network for Soft Robot Images Classification. In: 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 14-15 November 2020, Stockholm, Sweden.
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 16 Dec 2024 14:16 |
| Last Modified: | 16 Dec 2024 14:16 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18657 |
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