Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Article Swipe
Abdelrahman Alzarooni
,
Ehtesham Iqbal
,
Samee U. Khan
,
Sajid Javed
,
Brain Moyo
,
Yusra Abdulrahman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.11310
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.11310
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.11310
- https://arxiv.org/pdf/2501.11310
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406735046
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406735046Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.11310Digital Object Identifier
- Title
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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-20Full publication date if available
- Authors
-
Abdelrahman Alzarooni, Ehtesham Iqbal, Samee U. Khan, Sajid Javed, Brain Moyo, Yusra AbdulrahmanList of authors in order
- Landing page
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https://arxiv.org/abs/2501.11310Publisher landing page
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https://arxiv.org/pdf/2501.11310Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2501.11310Direct OA link when available
- Concepts
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Anomaly detection, Anomaly (physics), Computer science, Artificial intelligence, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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