Industrial defect detection plays a crucial role in ensuring product quality and enhancing enterprise competitiveness. Traditional defect detection methods rely on manual inspection， which is costly and inefficient， making it difficult to meet large-scale quality inspection requirements. In recent years， vision-based industrial defect detection technologies have made significant progress and become an efficient solution for product appearance quality inspection. However， in many practical industrial scenarios， it is challenging to obtain large amounts of labeled data， and there are requirements for both the labor cost and real-time performance of product detection， making unsupervised learning become a research hotspot. Related work on task construction， current technologies， evaluation standards， and the commonalities and differences among various methods in this field were reviewed. Firstly， the definition of industrial defect problems was clarified， and the difficulties of the problem were analyzed from perspectives of data challenges and task difficulties. Secondly， unsupervised deep learning-based methods for industrial defect detection were comprehensively introduced and systematically categorized. Furthermore， commonly used public datasets and evaluation metrics were summarized. Finally， future work in industrial defect detection was discussed.