Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects
Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects
复制标题
基于多尺度特征聚类的全卷积自动编码器，用于纹理表面缺陷的快速准确视觉检查
DOI：
10.1109/tase.2018.2886031
复制
发表时间：
2019-07-01
影响因子：
5.6
通讯作者：
Yin, Zhouping
中科院分区：
文献类型：
--
作者：
Yang, Hua;Chen, Yifan;Yin, Zhouping
由于各种表面纹理的外观变化巨大，纹理表面缺陷的目视检查在工业自动化领域仍然是一项具有挑战性的任务。由于手工特征的辨别能力低或其耗时的滑动窗口策略，当前的视觉检测方法无法同时有效地检测各种类型的纹理缺陷。在本文中，我们提出了一种新颖的基于无监督多尺度特征聚类的全卷积自动编码器（MS-FCAE）方法，该方法可以基于少量无缺陷纹理样本高效准确地检查各种类型的纹理缺陷。所提出的 MS-FCAE 方法利用不同尺度级别的多个 FCAE 子网络来重建多个纹理背景图像。残差图像是通过从输入图像中单独减去这些纹理背景得到的；然后，将它们融合成一幅缺陷图像。为了最大限度地提高效率，每个 FCAE 子网络都利用全卷积神经网络直接从输入图像中提取原始特征图。同时，每个FCAE子网络执行特征聚类以提高编码特征图的判别能力。所提出的 MS-FCAE 方法在多个纹理表面检测数据集上进行了定性和定量评估。该方法的精度达到 92.0%，而对于 1920 x 1080 像素的输入图像仅需要 82 ms。大量的实验结果表明，MS-FCAE实现了高效和最先进的检测精度。从业人员注意——大多数传统的视觉检测方法只能解决一种特定类型的纹理缺陷，而基于多尺度特征聚类的全卷积自动编码器（MS-FCAE）可以同时准确地检测各种类型的纹理表面缺陷，例如薄膜晶体管液晶显示器、木材、织物和瓷砖的纹理表面缺陷。此外，MS-FCAE只需要少量的表面纹理样本即可学习鲁棒的网络模型，并且其训练不需要缺陷样本。这对于工业应用极其重要，因为识别和标记缺陷样品很困难。此外，MS-FCAE可以利用基于图形处理单元的并行处理策略应用于在线视觉检测。
Visual inspection of texture surface defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background images. The residual images are obtained by subtracting these texture backgrounds from the input image individually; then, they are fused into one defect image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input images of 1920 x 1080 pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy.Note to Practitioners-Most conventional visual inspection methods can address only one specific type of texture defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no defect samples. This is extremely important for industrial applications because identifying and labeling defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.