## 1 Introduction  
Bainite is a significant component of steel that influences its strength and toughness. It can be obtained through isothermal transformation at temperatures that are optimal for the formation of pearlite and pre-eutectoid ferrite, which occurs at temperatures below the martensite start temperature [1]. Manual segmentation of steel images is a challenging task influenced by multiple factors, including the complexity of the steel surface, the quality of the image, and the specific goals of segmentation. Steel surfaces often exhibit irregularities, such as scratches, rust, stains, and uneven finishes, making it difficult to distinguish clear boundaries or features. The reflective nature of steel adds an additional complication, including glare, reflections, and shadows that can obscure important details, further complicating the segmentation process. The recognition of steel microstructure is complex and requires expertise [2, 3], and the classification process involves identifying and interpreting various phases, grains, and defects within a steel sample at a microscopic level using techniques like optical microscopy, SEM, or X-ray diffraction. Humans play a critical role in accurately recognizing these microstructural features; however, the task is challenging due to the intricate nature of steel's microstructure. Human subjectivity is a significant issue in steel image analysis. Analysts may interpret microstructure differently, leading to inconsistent results, and this variability is further compounded by factors such as fatigue, lapses in attention, and differences in experience, all of which can affect the accuracy and consistency of the analysis. Nevertheless, the characterization and classification of bainite present significant challenges due to the intricate, fine, and diverse nature of its constituent phases. Furthermore, the lack of consensus among experts and the inconsistency of nomenclature between classification schemes serve to complicate the process [4]. The existing classification methods are principally distinguished by their emphasis on the configuration of ferrite and carbon-rich phases. The most prevalent scheme differentiates between upper and lower bainite, with variants such as those featuring martensite-retained austenite (MA). Other approaches, such as those by [PERSON] et al. [5], provide a comprehensive framework identifying five bainite structures, including granular bainite with irregular ferrite and various carbon-rich phases. This systematic classification aids in the broader understanding and application of bainite microstructure in steel development [6]. Several approaches have been developed to classify phases in steels, focusing on bainite and related structures. [PERSON] et al. [7] applied a support vector machine (SVM) using morphological and textural parameters to classify carbon-rich phases and grouped all bainite structures into a single class without subclasses. Advanced methods based on electron backscatter diffraction (EBSD) have been used by [PERSON] et al. [5] and [PERSON] et al. [8] to distinguish granular, upper, and lower bainite, as well as bainite formed at different temperatures. [PERSON] et al. [9] employed segmentation for the identification of multiple phases. Other methods include analyzing morphological parameters of cementite precipitates ([PERSON] et al. [10]), substructure particle density and intensity values ([PERSON] et al. [11]), and regional contour patterns with local entropy ([PERSON] et al. [12]) for segmenting bainite alongside ferrite and martensite. [PERSON]'s study [6] demonstrates the use of textural parameters on images of benchmark samples, to distinguish various bainitic microstructures using an SVM. This method can also be classified as a classification problem, which can be solved using U-Nets [13]. Their ability to automatically extract hierarchical features makes them well suited for tasks such as bainitic phase identification and grain boundary detection.