Abstract
We would like to present a comprehensive study on the classification of iron ore pellets, aimed at identifying quality violations in the final product, alongside the development of an innovative imagebased measurement method utilizing the StarDist algorithm, which is primarily employed in the medical field. This initiative is motivated by the necessity to accurately identify and analyze objects within densely packed and unstable environments. The process involves segmenting these objects, determining their contours, classifying them, and measuring their physical dimensions. This is crucial because the size distribution and classification of pellets such as distinguishing between nice (quality) and joint (caused by the presence of moisture or indicating a process of production failure) types are among the most significant characteristics that define the quality of the final product. Traditional algorithms, including image classification techniques using Vision Transformer (ViT), instance segmentation methods like Mask R-CNN, and various anomaly segmentation algorithms, have not yielded satisfactory results in this context. Consequently, we explored methodologies from related fields to enhance our approach. The outcome of our research is a novel method designed to detect objects with smoothed boundaries. This advancement significantly improves the accuracy of physical dimension measurements and facilitates a more precise analysis of size distribution among the iron ore pellets. By leveraging the strengths of the StarDist algorithm, we aim to provide a robust solution that addresses the challenges posed by the complex nature of pellet classification and measurement.
Abstract (translated)
我们希望介绍一项关于铁矿石球团分类的全面研究，旨在识别最终产品中的质量违规情况，并开发一种创新的基于图像测量方法，该方法利用StarDist算法。尽管StarDist算法主要应用于医学领域，但我们希望通过这项研究解决在密集且不稳定环境中准确识别和分析对象的需求。这一过程包括分割这些对象、确定其轮廓、对其进行分类以及测量它们的物理尺寸。这非常重要，因为球团如区分优质（质量良好）与结块（由水分引起或表明生产过程中存在问题）类型时的大小分布和分类是定义最终产品质量的关键特征之一。 传统的算法，例如使用Vision Transformer (ViT)进行图像分类的技术、实例分割方法Mask R-CNN以及各种异常分割算法，在这种情况下未能提供令人满意的解决方案。因此，我们探索了相关领域的方法以改进我们的方法。研究结果是一种新型对象检测技术，该技术可以识别具有平滑边界的物体。这项进展显著提高了物理尺寸测量的准确性，并促进了铁矿石球团大小分布的更精确分析。 通过利用StarDist算法的优势，我们旨在提供一个强大的解决方案，解决由复杂性质导致的球团分类和测量挑战。
URL
https://arxiv.org/abs/2506.11126
