25. Development of Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines

This paper presents the development of user-friendly design and training tool for convolutional neural networks (CNNs) and support vector machines (SVMs) as an application development environment based on MATLAB. As the first test trial, an application of deep CNN (DCNN) for anomaly detection is developed and trained using a large number of images to distinguish undesirable small defects such as crack, burr, protrusion, chipping, spot, and fracture phenomena that occur in the production process of resin molded articles. Then, as the second test trial, a SVM incorporated with the AlexNet and another SVM incorporated with our original sssNet are, respectively, designed and trained to classify sample images into accepting as OK or rejecting as NG categories with high categorization rate. In the case of these SVMs, the training can be conducted by using only images of OK category. The AlexNet and the sssNet are different types of DCNNs, whose compressed feature vectors have 4096 and 32 elements, respectively. The two lengths of compressed feature vectors are used as the inputs for the two types of SVMs, respectively. The usability and operability of the developed design and training tool for DCNNs and SVMs are demonstrated and evaluated through training and classification experiments.