Automatic aerospace weld inspection using unsupervised local deep feature learning.

Automatic industrial inspection is critical to modern manufacturing enterprises. Due to their low cost and real-time processing speed, vision-based inspection systems are often used for this task. Deep Convolutional Neural Networks (CNNs) have been extensively applied to many computer vision tasks but require large numbers of annotated examples to train. Obtaining such annotations is expensive and time-consuming. In this study we describe a method which aims to make the best use of unannotated image data, which can often be collected easily. We propose a novel unsupervised local deep feature learning method based on image segmentation to build a network which can extract useful features from an image. The training algorithm alternates between (1) obtaining pseudo-labels by clustering the features extracted using a segmentation CNN and (2) training the CNN for feature learning using these pseudo-labels. To our knowledge, unsupervised local deep feature learning has not been addressed based on image segmentation in this way before. We demonstrate the approach on two aerospace weld inspection tasks. Our results show that the proposed unsupervised method performs almost as well as a CNN with the same architecture trained in a supervised manner. • Introduction of an unsupervised local deep feature learning method. • Developing an automatic aerospace weld inspection system on top of the proposed method. • Comparing the proposed method with the unsupervised classification CNN with the same backbone. [ABSTRACT FROM AUTHOR]