Automatic Detection of Sorbite Content in High Carbon Steel Wire Rod

Abstract

This paper presents a method for the automatic detection of sorbite content in high-carbon steel wire rods. A semantic segmentation model of sorbite based on DeepLabv3+ is established. The sorbite structure is segmented, and the prediction results are analyzed and counted based on the metallographic images of high-carbon steel wire rods marked manually. For the problem of sample imbalance, the loss function of Dice loss + focal loss is used, and the perturbation processing of training data is added. The results show that this method can realize the automatic statistics of sorbite content. The average pixel prediction accuracy is as high as 94.28%, and the average absolute error is only 4.17%. The composite application of the loss function and the enhancement of the data perturbation significantly improve the prediction accuracy and robust performance of the model. In this method, the detection of sorbite content in a single image only takes 10 s, which is 99% faster than that of 10 min using the manual cut-off method. On the premise of ensuring detection accuracy, the detection efficiency is significantly improved and the labor intensity is reduced.