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用图像处理方法开发和实施微结构缺陷检测模型

Sandra Gajoch¹, Dorota Wilk-Kołodziejczyk^1,2, Łukasz Marcjan¹

¹Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Krakow, Poland.

Materials (Basel, Switzerland)

|November 27, 2025

概括

这项研究开发了人工智能模型,用于检测 austempered柔性铁 (ADI) 微结构中的缺陷. ResNet,UNet和YOLO模型为分类,详细分析和工业缺陷检测提供了明显的优势.

科学领域:

材料科学材料科学材料科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

在钢化柔性铁 (ADI) 微结构中检测缺陷对于质量控制至关重要.
传统的缺陷分析方法可能耗时且主观.

研究的目的:

开发和实施人工智能 (AI) 模型,用于ADI微结构中的自动缺陷检测.
为了比较不同机器学习架构 (ResNet,UNet,YOLO) 对此任务的性能.

主要方法:

使用了三种机器学习方法:图像分类 (ResNet),像素分段 (UNet) 和对象检测 (YOLO).
数据准备涉及k-means集群,形态运算,二进制面具生成,标签转换和数据增强.
模型被调整并测试在一个定制的ADI微结构图像数据集上.

主要成果:

ResNet提供了高分类准确度,但缺乏缺陷定位.
UNet为定量分析生成了精确的细分口罩,但需要大量的计算资源,并与小缺陷作斗争.
通过使用边界框,YOLO 可快速检测缺陷.

结论:

每个AI模型都在ADI微结构缺陷分析中展示了针对特定应用的独特优势.
ResNet适用于初步分类,UNet适用于详细的实验室分析,YOLO适用于工业检测任务.

关键词:

这就是ResNet ResNet.这是一个YOLO YOLO.钢化柔性铁钢化柔性铁是一种柔性铁.造铁的造铁是什么

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