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A Novel Pixel-Wise Defect Inspection Method Based on Stable Background Reconstruction.

IEEE Transactions on Instrumentation and Measurement(2020)SCI 2区

Chinese Acad Sci

Cited 27|Views54
Abstract
In this article, an anomaly detection method based on background reconstruction is proposed to perform defect inspection on the texture surface of the industrial products. This method consists of two modules: 1) an autoencoder integrated with a generative adversarial network is utilized to reconstruct the textured background of the original image as a defect-free reference. Specifically, extra anomalous images are introduced and a mapping method of anomaly is given to improve the stability of reconstruction. 2) A U-net based inspection network is trained to perform pixel-wise analysis of the differences between the original and the reconstructed defect-free image. During these processes, only artificial synthesized defective images are utilized to train the model without any real defective samples. A series of experiments are conducted on several texture image data sets and the industrial production line. The experimental results reveal the effectiveness and versatility of the proposed method.
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Anomaly detection,autoencoder,background reconstruction,defect inspection
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要点】:本文提出了一种基于背景重建的异常检测方法,用于工业产品的纹理表面缺陷检测,通过结合自编码器和生成对抗网络重建稳定背景,并使用U-net网络进行像素级分析。

方法】:该方法包括两个模块,第一个模块使用集成了生成对抗网络的自编码器重建原始图像的纹理背景作为无缺陷的参考,并通过引入异常图像和给出异常映射方法来提高重建的稳定性;第二个模块使用基于U-net的网络进行训练,对原始图像和重建的无缺陷图像之间的差异进行像素级分析。

实验】:在多个纹理图像数据集上以及工业生产线进行了实验,使用人工合成的缺陷图像训练模型,实验结果表明了所提出方法的有效性和适应性。