工业应用中的异常检测：挑战、解决方案与未来方向综述
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review
AI总结：
本综述回顾自2019年以来的基于视觉的工业异常检测研究，涵盖IAD流水线组件、挑战、解决方案及未来方向，旨在提升工业检测自动化水平。
摘要：
利用相机传感器捕获的图像进行异常检测是工业层面的主流应用之一。特别是在先进制造和航空航天工程等多样化的工业任务中，它维护了生产过程中的质量并优化了效率。传统的异常检测工作流程基于人工操作员进行手动检查，这是一项繁琐的任务。智能自动化检测系统的进步彻底改变了工业异常检测（IAD）过程。近期基于视觉的方法能够利用计算机视觉自动提取、处理和解释特征，并与工业操作自动化的目标保持一致。鉴于检测方法的转变，本综述回顾了自2019年以来发表的研究，特别关注基于视觉的异常检测。本文呈现了现有综述中忽视的IAD流水线组成部分，包括与数据采集、预处理、学习机制和评估相关的领域。除了收集的出版物外，还强调了若干科学和工业相关的挑战及其展望的解决方案。同时总结了流行且相关的工业数据集，为检测应用提供了进一步的见解。最后，讨论了基于视觉的IAD的未来方向，为研究人员提供了工业检测最先进技术的见解。
英文摘要：
Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
