In the manufacturing process of aluminum alloy, the size, distribution, and shape of microscopic grains indicate the mechanical characteristics and product quality. However, for metallographic images that can reveal microstructures, the cost of expert labeling at pixel level is high. To solve the problem, we propose a semisupervised learning strategy for grain boundary detection with a few labeled images and abundant unlabeled samples. To expand the helpful information, transfer learning and rule-based region growing are considered. Specifically, a deep network used for extracting multiscale features is designed. With constant training, through a few labeled metallographic images and abundant transferred natural images, pseudo annotations are generated gradually for unlabeled metallographic images iteratively by feature similarity and boundary region growing. The increased unlabeled samples with their pseudo annotations would be involved in the following training process in semisupervised self-training mode to improve the generalization ability of model, together with the domain adaptation block. In experiments, the proposed two methods named semiricher convolutional features-generative adversarial networks (SemiRCF-GAN) and semiricher convolutional features-maximum mean discrepancy (SemiRCF-MMD) can effectively detect grain boundaries with only one labeled metallographic image, and achieve F1 scores of 0.73 and 0.72, respectively, which surpass typical methods.

Fusing prior knowledge with data-driven deep learning for measurement is interesting and challenging. For the detection of metallographic precipitations, the measurements of size and shape of precipitations are roughly predictable in advance through a transmission electron microscope (TEM). In this article, we proposed a novel instance segmentation network named prior mask R-CNN by fusing prior knowledge for automatic precipitation detection. On the basis of the typical mask R-CNN framework, we made the following improvements. First, at the bounding box classification stage, in order to restore area information, we input the size information besides only uniform dimension features after the region of interest align (RoIAlign). Second, at the mask segmentation stage, we proposed a new loss function based on normalized graph cuts. It is category-sensitive by setting different weight strategies for different categories based on their prior shapes. In addition, from the point of view of practicality, we designed an effective measurement extraction module to get specific measurements, such as the length of precipitations, from the final prediction results of our network. In a variety of experiments, our method achieves the highest mean average precision (mAP) of 0.475 and 0.298 among different famous methods for bounding box detection and mask segmentation tasks, respectively, which proves the effectiveness of our method.