Segmentation and Measurement of Superalloy Microstructure Based on Improved Nonlocal Block

The microstructure of superalloy materials has a decisive impact on its service performance. When preparing the material and photographing the microstructure, different depths of metallography perpendicular to the cut plane appear in the microstructure image. These metallographic features are major factors causing inaccurate segmentation. Aiming at the problems of traditional image processing methods, such as large noise and poor robustness of the edge extraction, the deep learning method is introduced. The receptive field of the traditional convolutional neural network method is too local to obtain remote dependencies, and dense feature extraction also brings problems such as excessive noise. To address the problem of non-tangential metallographic information in microstructure images that is not easily distinguishable. We consider how to combine and utilize the feature maps of the intermediate process as effectively as possible, and find that reusing affinity matrices from different angles and stacking them can improve the overall effect, and can solve the problem of inaccurate segmentation of metallographic images at different depths. We propose to improve and optimize the non-local attention module and further combine the module with the UNet network to form a new improved SNL-Unet image segmentation structure, which significantly raises the accuracy and efficiency of image segmentation, Additionally, we measure the characteristic parameters such as volume fraction, average thickness and the degree of rafting. The code for this paper will be available at github.com/ustbjdl1021/improved-snl-unet.