Automated surface inspection is an important task in many manufacturing
industries and often requires machine learning driven solutions. Supervised
approaches, however, can be challenging, since it is often difficult to obtain
large amounts of labeled training data. In this work, we instead perform
one-class unsupervised learning on fault-free samples by training a deep
convolutional neural network to complete images whose center regions are cut
out. Since the network is trained exclusively on fault-free data, it completes
the image patches with a fault-free version of the missing image region. The
pixel-wise reconstruction error within the cut out region is an anomaly image
which can be used for anomaly detection. Results on surface images of decorated
plastic parts demonstrate that this approach is suitable for detection of
visible anomalies and moreover surpasses all other tested methods.

通过使用一类无监督学习方法，使用深度卷积神经网络进行图像完成，以完成缺件区域的图像，并使用像素重建误差进行异常检测的方法，有效用于检测表面装饰塑料零件的显著异常，并且此方法优于其他测试方法。