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Proceedings of the 8th IIAE International Conference on Industrial Application Engineering 2020

Orientation Detection Using a CNN Designed by Transfer Learning of AlexNet

Fusaomi Nagata, Kohei Miki, Yudai Imahashi, Kento Nakashima, Kenta Tokuno, Akimasa Otsuka, Keigo Watanabe and Maki K. Habib

pp. 295

299

The Institute of Industrial Applications Engineers

The Institute of Industrial Applications Engineers

Artificial neural network (ANN) which has four or more layers structure is called deep NN (DNN) and is recognized as a promising machine learning technique. Convolutional neural network (CNN) has the most used and powerful structure for image recognition. It is also known that support vector machine (SVM) has a superior ability for binary classification in spite of only two layers. We have developed a CNN&SVM design and training tool for defect detection of resin molded articles, and the effectiveness and validity have been proved through several CNNs design, training and evaluation. The tool further enables to easily design a CNN model based on transfer learning concept. In this paper, a CNN acquired by transfer learning of AlexNet, which is the winner of ImageNet LSVRC2012, is designed to recognize the orientation of objects. The effectiveness of the transfer learning based CNN is evaluated using test data set.