Label Noise Learning Method for Metallographic Image Recognition of Heat-Resistant Steel for Use in Pressure Equipment

• Published in: Materials Vol. 15; no. 19; p. 7037
• Main Authors: Shen, Zhiyuan, Hu, Haijun, Huang, Ziyi, Zhang, Yu, Wang, Yafei, Li, Xiufeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: Accuracy; Classification; Datasets; Deep learning; Heat; Heat resistant steels; heat-resistant steel; label noise learning; metallographic image recognition; Methods; Molybdenum; Molybdenum steel; Object recognition; Pearlite; Pressure vessels; Reliability analysis; Spheroidizing; Steel; China
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15197037

In metallographic examination, spherular pearlite gradation, an important step in a metallographic examination, is the main indicator used to assess the reliability of heat-resistant steel. Recognition of pearlite spheroidization via the manual way mainly depends on the subjective perceptions and experience of each inspector. Deep learning-based methods can eliminate the effects of the subjective factors that affect manual recognition. However, images with incorrect labels, known as noisy images, challenge successful application of image recognition of deep learning models to spherular pearlite gradation. A deep-learning-based label noise method for metallographic image recognition is thus proposed to solve this problem. We use a filtering process to pretreat the raw datasets and append a retraining process for deep learning models. The presented method was applied to image recognition for spherular pearlite gradation on a metallographic image dataset which contains 422 images. Meanwhile, three classic deep learning models were also used for image recognition, individually and coupled with the proposed method. Results showed that accuracy of image recognition by a deep learning model solely is lower than the one coupled with our method. Particularly, accuracy of ResNet18 was improved from 72.27% to 77.01%.