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基于改进U-Net的喷射成形高速钢碳化物提取算法

An improved U-Net algorithm for extracting carbides from spray formed high speed steel
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摘要 针对当前利用数字图像处理算法及深度学习模型对钢中碳化物进行定量分析时,存在准确率低、提取效果不佳导致的分析误差较大等问题,提出一种基于U-Net改进的喷射成形高速钢碳化物分割算法GSG-Unet,旨在对钢中不同种类的碳化物进行准确高效地分割提取,以便进行自动化定量分析。通过添加ConvNeXt模块和CBAM注意力机制加强了模型的特征提取能力和处理漏检问题能力,使分割效果有显著提升。结果表明,改进后模型的准确率、召回率、类平均交并比和骰子系数分别为91.31%、87.52%、84.89%和83.16%,较原模型有较大提升。该模型能够精准地将MC碳化物和M_(6)C碳化物从马氏体基体上进行分割,为快速准确地进行高速钢中碳化物的自动化定量分析提供了有力技术支持。 To address the issues of low accuracy,poor extraction effect,and large analysis errors caused by using digital image processing algorithms and deep learning models for quantitative analysis of carbides in steel,a modified jet forming high speed steel carbides segmentation algorithm based on U-Net(GSG-Unet)was proposed.The aim is to accurately and efficiently segment and extract different types of carbides in steel for automated quantitative analysis.The model is strengthened by adding ConvNext module and CBAM attention mechanism to enhance its feature extraction capability and ability to handle missed detections,resulting in significant improvement in segmentation performance.The results show that the improved model has an accuracy of 91.31%,recall rate of 87.52%,class-average intersection over union of 84.89%,and Dice of 83.16%,which are significantly higher than those of the original model.This improved model can accurately segment MC and M_(6)C carbides from martensite matrix,providing strong technical support for rapid and accurate automated quantitative analysis of carbides in high speed steel.
作者 陈家树 侯国栋 周继宽 刘天琪 邓百川 张祥林 Chen Jiashu;Hou Guodong;Zhou Jikuan;Liu Tianqi;Deng Baichuan;Zhang Xianglin(School of Materials Science and Engineering,Huazhong University of Science and Technology,Wuhan Hubei 430074,China;Heye Special Steel Co.,Ltd.,Shijiazhuang Hebei 052165,China;Hubei Huishengbai Mold Material Technology Co.,Ltd.,Wuhan Hubei 430080,China)
出处 《金属热处理》 CAS CSCD 北大核心 2023年第10期87-93,共7页 Heat Treatment of Metals
关键词 高速钢 碳化物 自动化定量分析 深度学习 语义分割 high speed steel carbides automated quantitative analysis deep learning semantic segmentation
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