Fastener Classification Using One-Shot Learning with Siamese Convolution Networks - Tastimur, Canan; Akin, Erhan (Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2022)
Deep Learning has been widely used in image-based applications such as object classification, object detection, and object recognition in recent years. Classifying highly similar objects is a very difficult problem. It is difficult to classify datasets in this situation where object similarity between classes and differences between classes are high. In this study, Siamese Convolution Neural Network, which is a similarity measurement-based network, has been practiced to classify 6 types of screws, 5 types of nuts, and 7 types of bolts that are very similar to each other. In addition, this neural network formed with the One-Shot Learning technique is trained. Thanks to the OSL technique, there is no need to use large data sets. Also, there is no need to use large amounts of data from each class. Adding a new class to be classified is also made easier by the use of the OSL technique. The performance results of the proposed method are manifested in detail in the article.
Keywords: Classification; Deep learning; Fastener; Siamese network; One-shot learning