Omkar2118/datasets huggingface dataset:
Aachen-Heerlen Annotated Steel Microstructure Dataset

​The Aachen-Heerlen annotated steel microstructure dataset comprises 1,705 scanning electron microscopy (SEM) images of bainitic steel samples. Each image is annotated with expert-delineated polygons highlighting martensite-austenite (MA) islands—complex blocky structures that significantly influence the mechanical properties of steel. Additionally, the dataset includes metadata detailing the chemical composition, transformation temperatures, and cooling rates of the steel samples.​

Motivation and Summary of Content

The primary motivation for creating this dataset is to facilitate the development of machine learning models capable of automatically and accurately detecting MA islands in steel microstructures. Manual identification of these structures is labor-intensive and prone to subjectivity. By providing a comprehensive set of annotated images, the dataset aims to enhance reproducibility and efficiency in microstructural analysis within materials science.​

Potential Use Cases

Materials Science Research: Researchers can utilize this dataset to explore the relationship between the morphology of MA islands and the mechanical characteristics of bainitic steel, potentially leading to improved material design and processing techniques.​

Computer Vision Applications: The dataset serves as a valuable resource for training and evaluating object segmentation models, particularly in recognizing and delineating complex geometries like MA islands. This can advance the development of machine learning algorithms tailored for materials characterization.​

Quality Control in Steel Manufacturing: Automated detection and analysis of MA islands can be integrated into quality control processes, enabling more consistent and objective assessments of steel microstructures during production.​

By bridging the gap between materials science and machine learning, this dataset fosters interdisciplinary approaches to understanding and optimizing steel microstructures.​

Source: https://www.nature.com/articles/s41597-021-00926-7