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首页> 外文期刊>Journal of visualization >Shape evaluation of highly overlapped powder grains using U-Net-based deep learning segmentation network
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Shape evaluation of highly overlapped powder grains using U-Net-based deep learning segmentation network

机译：基于U-NET的深度学习分割网络形状评估高度重叠的粉末粒度

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摘要

With the advancement of electron microscopy, industrial microscale objects are analyzed through image-based characterization. However, the automated and objective assessment of a vast number of images required for quality control is limited by the incomplete segmentation of individual objects in the image. In this study, the scanning electron microscope images of powder grains are selected as target images representing industrial microscale objects. A deep neural network based on the U-Net is developed and trained by manually labeled ground truth. Although the U-Net is a basic network originally devised for biomaterials, the network in this study achieves approximately 90% accuracy and outperforms conventional thresholding methods. However, the boundaries distinguishing individual are not completely classified. The inference results are further processed with morphological operations and watershed algorithms to quantitatively measure grain shapes. Discrepancies in shape parameters between ground truth and network prediction are also discussed.
机译：随着电子显微镜的进步，通过基于图像的表征分析工业微观物体对象。然而，质量控制所需的大量图像的自动和客观评估受到图像中各个物体的不完整分割的限制。在该研究中，选择粉末晶粒的扫描电子显微镜图像作为表示工业微尺度物体的目标图像。通过手动标记的地面真理开发和培训基于U-Net的深度神经网络。虽然U-Net是最初设计用于生物材料的基本网络，但本研究中的网络实现了大约90％的精度和常规常规阈值化方法。然而，区分个体的边界没有完全分类。推断结果进一步处理了形态学操作和流域算法，以定量测量晶粒形状。还讨论了地面真理与网络预测之间的形状参数的差异。

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