Classification of titanium microstructure with fully convolutional neural networks. (April 2019) - Mongkhonthanaphon, S; Limpiyakorn, Y
Abstract: Titanium and its alloy exhibit excellent properties for biomedical applications, especially in implant surgery. Classification of Titanium microstructure is the process in material inspection that reveals background of the material. Generally, microstructure classification is manually performed. Due to the complexity of microstructure features, expertise is required for process operation. The traditional classification by humans is time consuming and possibly error prone if the inspection is not performed by titanium microstructure experts. Deep learning is considered the revolution of computer vision to enable computers to see and perceive like humans. The technique is widely used for automatically classifying images with high accuracy. In order to reduce human inspection time during quality control, this research presents the use of a type of deep learning, Fully Convolutional Neural Networks, for pixel-wise classification in the titanium microstructure images. The dataset contains private images of titanium samples taken by SEM microscopes. As the available training dataset is small, data augmentation using elastic deformations is applied for increasing the accuracy of the model. Constructed with the U-net architecture, the network achieves good performance with the pixel accuracy of 92.67% and mean IoU of 71.30%.