A Convolutional Neural Network for Soft Robot Images Classification
| Autor: | Victoria Oguntosin, Ayoola Akindele, Aiyudubie Uyi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Training set Computer science business.industry Feature extraction Soft robotics 02 engineering and technology Convolutional neural network 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence Actuator business |
| Zdroj: | 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). |
| DOI: | 10.1109/iscmi51676.2020.9311562 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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