基于深度学习的钢材金相图像晶粒度等级评定方法

A deep learning-based method for grading the grain size of steel metallographic images

准确地评定钢材金相组织晶粒度等级能检测材料劣化情况,保障设备的服役安全。针对传统人工评定钢材金相组织晶粒度等级的方法耗时久且易受人工经验影响,评价结果一致性差且不可重复等问题,提出了一种基于深度学习的钢材金相晶粒度等级评定方法。在U-net模型上添加跳跃连接层并减少下采样次数来提高模型的分割准确率并减少网络参数量,在117张验证集上的像素准确率达93.86%,平均像素准确率(mean pixel accuracy,MPA)达86.89%,网络参数量仅为2.02 M。对晶界预测结果进行数字图像处理并结合截点法进行晶粒度等级评定,在测试图像上评定钢材晶粒度等级平均耗时仅8.3 s/张。与人工评级方法相比,本文方法具有准确性、高效性及可重复性。

Accurate assessment of the metallographic grain size grade of steel can detect material deterioration and ensure the safety of equipment in service.In order to solve the problems that the traditional manual evaluation of steel metallographic grain size grade is time-consuming and easily influenced by manual experience,the evaluation results are not consistent and irreducible,etc,a deep learning-based steel metallographic grain size grade evaluation method is proposed.By adding a jump connection layer to the U-net model and reducing the number of downsampling to improve the segmentation accuracy and reduce the number of network parameters,the pixel accuracy is 93.86%and the mean pixel accuracy(MPA)is 86.89%on the 117 validation sets.The number of network parameters is only 2.02 M.The grain boundary prediction results are digitally processed and combined with the intercept point method to grade the grain size,and the average time taken to grade the grain size of steel on the test image is only 8.3 s/sheet.Compared with manual rating methods,this method is accurate,efficient and repeatable.