Survey on industrial defect detection with deep learning

Abstract

Deep-learning-based industrial defect detection methods play an increasingly important role in intelligent manufacturing, as they provide compelling benefits in reducing the cost spent on manual product inspection, while improving inspection accuracy and efficiency. They have been widely used in various manufacturing, operation and maintenance (O&M) applications such as automated inspection, smart patrol, and quality control. This survey aims to make a comprehensive introduction of industrial defect detection, which mainly spans its definition, difficulties, challenges, mainstream methods, open datasets, and evaluation protocols, so as to help researchers gain rapid and comprehensive knowledge. Specifically, we firstly introduce some background knowledge. Secondly, based on the difference of the provided annotations of different datasets in practical scenarios, we categorize most methods into three task settings: known defects, unknown defects, and few-shot defects. We go over these methods in further depth and illustrate a detailed analysis. We expound the connections between different algorithms and actual demands to present a clear picture of how different algorithms evolve. Thirdly, this paper summarizes some useful strategies that can effectively improve defect detection performance. Finally, based on our understanding of this area, we conclude several limitations of existing methods in practical applications as well as several directions of future research that embrace further efforts.