arXiv 2410.219822025-03-21cs.CV

RGB、3D及多模态无监督工业图像异常检测方法综述

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

Fudan University（复旦大学）；Free University of Bozen-Bolzano（博岑-博尔扎诺自由大学）；Shanghai Ocean University（上海海洋大学）；Shenzhen University（深圳大学）

Yuxuan Lin, Yang Chang, Xuan Tong, Jiawen Yu, Antonio Liotta, Guofan Huang, Wei Song, Deyu Zeng, Zongze Wu, Yan Wang, Wenqiang Zhang

备注：: Accepted by Information Fusion

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AI总结：

针对现有无监督工业图像异常检测综述多聚焦RGB模态、缺乏3D及多模态相关讨论的问题，本文全面梳理三种模态下的UIAD任务，涵盖概念、数据集、方法与融合策略，总结挑战并展望未来方向，提供相关开源资源。

摘要：

在工业信息化发展进程中，无监督异常检测技术有效克服了异常样本稀缺的问题，显著提升了智能制造的自动化水平与可靠性。工业图像异常检测作为其重要分支，聚焦于通过计算机视觉技术自动识别工业场景中的视觉异常（如产品表面缺陷、装配错误、设备外观异常等）。随着无监督工业图像异常检测（Unsupervised industrial Image Anomaly Detection, UIAD）的快速发展，该技术不仅在RGB设置下取得了优异的检测性能，在3D及多模态（RGB与3D结合）设置下也有出色表现。然而，现有综述主要聚焦于RGB设置下的UIAD任务，对3D与多模态设置的讨论甚少。为填补这一空白，本文对三种模态设置下的UIAD任务进行了全面综述。具体而言，本文首先介绍了UIAD的任务概念与流程；随后梳理了RGB、3D、多模态三种模态设置下的UIAD研究，涵盖相关数据集与方法，并综述了多模态设置下的多模态特征融合策略；最后总结了三种模态设置下UIAD任务面临的主要挑战，并对未来发展方向提出了见解，旨在为研究人员提供全面参考，为工业信息化发展提供新视角。相关资源可访问https://github.com/Sunny5250/Awesome-Multi-Setting-UIAD获取。

英文摘要：

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://github.com/Sunny5250/Awesome-Multi-Setting-UIAD.

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。