Automatic segmentation of lamellar eutectoids in iron oxide scales using optimized U-net network

Abstract

The variation in lamellar spacing of eutectoids in iron oxide affects the surface quality of the hot-rolled strip and its subsequent deep processing. In order to investigate the lamellar spacing numerically, an accurate segmentation of Fe and Fe3O4 phases is required in the structural picture. In this paper, a dataset was generated for identifying Fe and Fe3O4 in eutectoids. Also, a squeeze-and-excitation network module was integrated for improving the accuracy in image segmentation. The obtained experimental results show that the PA and mIoU could reach 96.83% and 94.49% in optimized U-net, indicating its excellent segmentation capability compared with traditional image segmentation networks. Using this method, fast and accurate extraction of the characteristic information about the picture can be realized, which would help in investigating the phase transition behavior of the eutectoid transformation in the Fe–O system.