UNSUPERVISED AND SEMI-SUPERVISED LEARNING IN AUTOMATIC INDUSTRIAL IMAGE INSPECTION
It has been widely studied in industry production environment to apply computer version onX-ray images for automatic visual inspection. Traditional methods embrace image processingtechniques and require custom design for each product. Although the accuracy of this approachvaries, it often fall short to meet the expectations in the production environment. Recently, deeplearning algorithms have significantly promoted the capability of computer vision in various tasksand provided new prospects for the automatic inspection system. Numerous studies appliedsupervised deep learning to inspect industrial images and reported promising results. However,the methods used in these studies are often supervised, which requires heavy manual annotation.It is therefore not realistic in many manufacturing scenarios because products are constantlyupdated. Data collection, annotation and algorithm training can only be performed after thecompletion of the manufacturing process, causing a significant delay in training the models andestablishing the inspection system. This research was aimed to tackle the problem usingunsupervised and semi-supervised methods so that these computer vision-based machine learningapproaches can be rapidly deployed in real-life scenarios. More specifically, this dissertationproposed an unsupervised approach and a semi-supervised deep learning method to identifydefective products from industrial inspection images. The proposed methods were evaluated onseveral open source inspection datasets and a dataset of X-Ray images obtained from a die castingplant. The results demonstrated that the proposed approach achieved better results than otherstate-of-the-art techniques on several occasions.
- Doctor of Philosophy
- Computer and Information Technology
- West Lafayette