A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts.

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-06-23 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1554196
Vinita Shukla, Amit Shukla, Surya Prakash S K, Shraddha Shukla
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引用次数: 0

Abstract

Industrial automation is rapidly evolving, encompassing tasks from initial assembly to final product quality inspection. Accurate anomaly detection is crucial for ensuring the reliability and robustness of automated systems. The intelligence of an industrial automation system is directly linked to its ability to detect and rectify abnormalities, thereby maintaining optimal performance. To advance intelligent manufacturing, sophisticated methods for high-quality process inspection are indispensable. This paper presents a systematic review of existing deep learning methodologies specifically designed for image anomaly detection in the context of industrial manufacturing. Through a comprehensive comparison, traditional techniques are evaluated against state-of-the-art advancements in deep learning-based anomaly detection methodologies, including supervised, unsupervised, and semi-supervised learning methods. Addressing inherent challenges such as real-time processing constraints and imbalanced datasets, this review offers a systematic analysis and mitigation strategies. Additionally, we explore popular anomaly detection datasets for surface defect detection and industrial anomaly detection, along with a critical examination of common evaluation metrics used in image anomaly detection. This review includes an analysis of the performance of current anomaly detection methods on various datasets, elucidating strengths and limitations across different scenarios. Moreover, we delve into the domain of drone-based, manipulator-based and AGV-based anomaly detections using deep learning techniques, highlighting the innovative applications of these methodologies. Lastly, the paper offers scholarly rigor and foresight by addressing emerging challenges and charting a course for future research opportunities, providing valuable insights to researchers in the field of deep learning-based surface defect detection and industrial image anomaly detection.

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系统综述:基于深度学习的图像异常检测在工业检测中的作用。
工业自动化正在迅速发展,包括从初始组装到最终产品质量检查的任务。准确的异常检测对于保证自动化系统的可靠性和鲁棒性至关重要。工业自动化系统的智能与其检测和纠正异常的能力直接相关,从而保持最佳性能。为了推进智能制造,高质量的过程检测方法是必不可少的。本文对现有的深度学习方法进行了系统的回顾,这些方法是专门为工业制造背景下的图像异常检测设计的。通过综合比较,传统技术与基于深度学习的最新异常检测方法(包括监督、无监督和半监督学习方法)进行了评估。针对诸如实时处理限制和不平衡数据集等固有挑战,本综述提供了系统的分析和缓解策略。此外,我们还探索了用于表面缺陷检测和工业异常检测的流行异常检测数据集,以及用于图像异常检测的常见评估指标的关键检查。这篇综述分析了当前异常检测方法在不同数据集上的性能,阐明了不同场景下的优势和局限性。此外,我们还使用深度学习技术深入研究了基于无人机、基于机械手和基于agv的异常检测领域,重点介绍了这些方法的创新应用。最后,本文通过解决新出现的挑战并为未来的研究机会绘制路线,提供了学术严谨性和前瞻性,为基于深度学习的表面缺陷检测和工业图像异常检测领域的研究人员提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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