Using Image Data for Machine Learning-Based Defect Detection

Abstract

The inspection process, which was previously based primarily on human sampling, has now been fully automated by the introduction of inspection machines that use images. However, depending on the inspection conditions of the system, there is a risk of the over- or non-detection of defective products. This problem may result in a loss for companies that have implemented automated inspection machines. Therefore, with the introduction of such machines, the establishment of discrimination techniques for defect detection has become a critical issue. This study aimed to develop a method for automatically detecting defects in image data with high accuracy. This study used images of metal products. We attempted to detect defects inside metal products. This study proposed a method using machine learning. Additionally, this research made recommendations for data preprocessing that may be required before machine learning. Then, we chose the best combination of the proposed machine learning approach and data preprocessing, that is the most capable of detecting defects, using the L16 orthogonal table or ANOVA. The classification accuracy exceeded 90% using the proposed method, and we partly solved the problem thrown up by previous studies.