Industrial defect detection is a critical component of modern industrial production and operation, ensuring product quality, production efficiency, and safety. The complex logical reasoning and generalization capabilities of large models have positioned them as the critical force behind the new wave of artificial intelligence. With the emergence of large models, a new paradigm is established for industrial defect detection, bringing both fresh opportunities and challenges. This article provides a comprehensive review of the current application status of large models in the field of industrial defect detection. Firstly, the development process of large models is systematically combed, and the core technologies are introduced in detail, including model architecture, multimodal data processing, pre-training techniques, fine-tuning methods, alignment strategies and efficient reasoning mechanisms. Secondly, a survey of traditional methods based on machine learning and deep learning for industrial defect detection is provided, followed by a comparison with large model-based approaches and a summary of their respective strengths and limitations. Then, focusing on the industrial defect detection domain, the review introduces commonly used open-source datasets that support large model research and evaluation, as well as the performance evaluation methods of large models. Furthermore, it categorizes the current main application of large models into five directions, including defect detection and localization, defect detection in complex scenarios and micro-defect detection, few-shot and zero-shot adaptive detection, interactive defect analysis and decision support, and defect data generation with automatic annotation. Finally, this article thoroughly analyzes the challenges confronting large models in industrial defect detection, such as data quality and security, high-reliability requirements, cost constraints and sustainable development, and the lack of unified evaluation standards, while providing an outlook on their future trends. The review aims to provide valuable references and insights for the continued advancement and broader implementation of large models in industrial defect detection.