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.
Highlights
Aluminum alloy products have been widely used in machinery manufacturing, transportation, electrical, shipbuilding, automobile, aviation, aerospace, chemical industry, construction and other fields [1,2,3,4]
We summarized the contributions of this paper as follows: (1) We implemented the instance segmentation framework of metallographic image, which could achieve automatic microstructure instance segmentation for given aluminum alloy metallographic images and provide a more effective tool for the quantitative analysis of metallographic images
In order to verify the effectiveness of our proposed method, a large number of experiments were performed for the analysis of instance segmentation performance
Summary
Aluminum alloy products have been widely used in machinery manufacturing, transportation, electrical, shipbuilding, automobile, aviation, aerospace, chemical industry, construction and other fields [1,2,3,4]. For metallographic image segmentation, the commonly used classifiers have included multilayer perceptron [19], random forest [20], optimum-path forest [21], neural network [22] and support vector machine (SVM) [23,24] These methods often outperform the image processing-based methods. In reference [29], the DeepLab network was applied to segment Al-La alloy metallographic images These deep learning-based methods achieve satisfactory results, but they are unable to identify microstructure instances. We summarized the contributions of this paper as follows: (1) We implemented the instance segmentation framework of metallographic image, which could achieve automatic microstructure instance segmentation for given aluminum alloy metallographic images and provide a more effective tool for the quantitative analysis of metallographic images.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
