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Vision-based inspection and segmentation of trimmed steel edges

机译：基于视觉的修剪钢边缘检查和分割

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This paper deals with the automated online-inspection of trimmed edges of heavy steel plates. These edges sometimes exhibit defective fractured areas and burr. A novel algorithm is presented, which segments the images recorded by a CCD camera into burnished area, defective fractured area, burr, and area which exhibits a high quality. The segmented images will serve as a basis for quality control, generating data for machine learning, and informing the plant operator in case of insufficient quality. The segmentation is performed via thresholding, morphologic operations, estimation methods as well as statistical methods. The underlying framework is presented and example images demonstrate the capability of the algorithm to segment trimmed edges with various properties.
机译：本文介绍了自动在线检查重型钢板修边的方法。这些边缘有时会出现缺陷的破裂区域和毛刺。提出了一种新颖的算法，该算法将CCD摄像机记录的图像分割为抛光区域，缺陷破裂区域，毛刺和表现出高质量的区域。分割后的图像将用作质量控制的基础，生成用于机器学习的数据，并在质量不足的情况下通知工厂操作员。分割是通过阈值化，形态运算，估计方法以及统计方法来执行的。呈现了基础框架，示例图像演示了该算法分割具有各种属性的修剪边缘的能力。

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