A Systematic Literature Review of the Application of Artificial Image Data for Visual Defect Detection

Artificial images for defect detection have gained growing importance in the recently developed defect detection architectures. This systematic literature review examines the use of artificial image data for visual defect detection. It also provides an overview of the methods used for this purpose. Following PRISMA guidelines, the review analyses existing literature to identify key areas where artificial image generation is being applied or could be implemented to enhance defect detection capabilities. It was outlined that data augmentation is currently the most commonly used image generation method to improve individual datasets for defect detection tasks. The results provide insights into the current state of the art and potential future directions for addressing data scarcity in visual defect detection. The knowledge gained in this study can be used by researchers and users to find suitable methods for the desired field of application in order to overcome the problem of data scarcity, and the findings also show where there is still a need for research.