Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review

2025-01-20

AI Summary

Industrial visual anomaly detection (IAD) faces critical challenges including scarce labeled data, domain shift, and insufficient localization interpretability. To address these, this work presents a systematic survey of over 120 IAD studies published since 2019. Methodologically, it establishes the first comprehensive pipeline taxonomy—spanning data acquisition, domain-adaptive preprocessing, modeling, and evaluation—filling key gaps in prior surveys regarding industrial image modeling, domain-aware preprocessing, and interpretable localization assessment. It introduces a cross-domain “problem–solution” mapping framework that bridges academic methodologies with real-world production-line requirements; structurally catalogs mainstream industrial datasets and evaluation protocols; and distills five fundamental technical bottlenecks alongside corresponding mitigation strategies. The contributions include a reusable methodology guide and a forward-looking research roadmap, providing both theoretical foundations and practical guidance for deploying unsupervised and weakly supervised solutions in industrial quality inspection.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• Quality Inspection
• Anomaly Detection
• Automation

Innovation

• Visual Anomaly Detection
• Industrial Applications
• Automated Quality Control