综述工业缺陷检测的现状与未来，聚焦真实场景下的挑战与新方法。
A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
- 对比2D/3D模态下闭集与开集检测方法的演进
- 指出开集检测可减少标注依赖并识别新型缺陷
- 适合关注工业质检智能化的工程师与研究者
工业缺陷检测对保障现代制造产品的质量至关重要。随着对精度、自动化和可扩展性的要求不断提高，传统检测方法日益难以满足现实需求。计算机视觉与深度学习的显著进展大幅提升了二维与三维模态下的缺陷检测能力。一个重要趋势是从闭集向开集缺陷检测框架转变，降低对大量缺陷标注的依赖，实现对新型异常的识别。尽管取得诸多进展，当前仍缺乏对工业缺陷检测领域的系统性认知。本文深入分析了2D与3D模态中闭集与开集检测策略的发展历程，突出开集方法的日益重要性。梳理实际检测环境中存在的关键挑战，揭示新兴趋势，为该快速发展的领域提供当前且全面的视角。
原文摘要 · Abstract (English)
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.
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