Microstructure Instance Segmentation from Aluminum Alloy Metallographic Image Using Different Loss Functions

Abstract

Automatic segmentation of metallographic image is very important for the implementation of an automatic metallographic analysis system. In this paper, a novel instance segmentation framework of a metallographic image was implemented, which can assign each pixel to a physical instance of a microstructure. In this framework, we used the Mask R-CNN as the basic network to complete the learning and recognition of the latent feature of an aluminum alloy microstructure. Meanwhile, we implemented five different loss functions based on this framework and compared the influence of these loss functions on metallographic image segmentation performance. We carried out several experiments to verify the effectiveness of the proposed framework. In these experiments, we compared and analyzed six different evaluation metrics and provided constructive suggestions for the performance evaluation of metallographic image segmentation method. A large number of experimental results have shown that the proposed method can achieve the instance segmentation of an aluminum alloy metallographic image and the segmentation results are satisfactory.