# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 1 Introduction
Cropped images of pearlite and ferrite are strategically placed on these templates to synthesize accurately labeled ferrite-pearlite microstructures. Subsequently, a deep learning architecture, UNET, is trained using synthetic microstructures and tested on real microstructures. The results, compared with manually annotated microstructures, demonstrate a prominent level of agreement, reaching an accuracy of about 98%. [PERSON] et al. [16] conducted a study that focuses on recognizing the microconstituents of ferrite and pearlite and making predictions of their mechanical properties. For this purpose, they elaborate a residual U-shaped network based on ResNet32 to identify grain boundaries and their size, obtaining better segmentation results than the conventional neural network FCN-8s, reaching over 93% in frequency weighted intersection over union (FWIoU). [PERSON] et al. [17] utilized fully convolutional networks (FCNs) along with a max-voting scheme for the classification of martensite, bainite, pearlite, and ferrite phases in low-carbon steels, achieving a classification accuracy of 93.94%. Recently, works similar to the research in this paper have been published, such as [PERSON] et al. [18] that accomplished the successful classification of ferrite-martensite dual-phase steel microstructures through the implementation of the U-Net model and achieved pixel-wise accuracies of around 98%, as well as [PERSON] et al. [19], who provided a comparison with different segmentation architectures for steel micrographs like DeepLabv3+, Enet, Unet, and PSPnet. They propose a new semantic network based on the improvement of a fully convolutional network (FCN) with the atrous spatial pyramid pooling (ASPP) technique for feature extraction, surpassing the previous ones according to the metric Intersection over Union (IoU), achieving a performance of up to 80.43%. In our specific study, we employed LOM images as opposed to the SEM images used in the referenced article. This choice might introduce differences in the characteristics and features of the micrographs, potentially impacting the performance of segmentation algorithms. It is worth noting that the selection of imaging modalities can influence the choice of segmentation techniques and their effectiveness in each context. [PERSON] et al. [20] conducted training on two datasets comprising images of steel alloys, one consisting of carbide and the other predominantly of ferrite microconstituents. They employed PSPNet and DeepLabv3+ with ResNet18 segmentation networks. The authors proposed enhancing the receptive field of the convolutional neural network (CNN) to improve contextual perception of images without altering the network architecture. This was achieved by scaling the original image size to 0.5 times during image loading. Additionally, the authors established an automated quantitative analysis of the microstructures using OpenCV software after segmentation, extracting morphological information from classified pixels to obtain the average carbide radius and the number of carbides. The results, evaluated on original large-size images, yielded a mean Intersection over Union (mIoU) score of approximately 80%. Additionally, [PERSON] et al. [21] present, in this case in the context of mudrock SEM images, a method for filtering and segmentation using deep learning to identify pore and grain features named MudrockNet, which is based on DeepLab-v3+. The predictions for the test data obtain a mean IoU of 0.6663 for silt grains, 0.7797 for clay grains, and 0.6751 for pores. Automated phase identification in steel microstructures is a rapidly evolving field. While previous studies have addressed segmentation challenges with varying degrees of success, several issues remain unresolved, including the application of segmentation to low-magnification optical images and the scarcity of dedicated steel microstructure image databases.