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Defect Detection in SEM Images of Nanofibrous Materials
Open accessAbstract
Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial production.
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Existing datasets for anomaly segmentation are limited to the inspection of textured surfaces or focus on novelty detection in multi-class semantic segmentation scenarios.
“Many of them are limited to the inspection of textured surfaces or focus on novelty detection in multi-class semantic segmentation scenarios.”
From The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection (2021) · cites this paper as Carrera et al. (2017)
Existing datasets lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations.
“In particular, they lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations.”
From Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization (2022) · cites this paper as Carrera et al. 2017
Existing datasets for unsupervised anomaly detection and localization are biased towards local structural anomalies such as scratches, dents, or contaminations.
“We find that existing datasets are biased towards local structural anomalies such as scratches, dents, or contaminations.”
From Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization (2022) · cites this paper as Carrera et al. 2017
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- 0, Electrospinz sem analyser, electrospinz Ltd
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“Dataset Class Normal Abnormal Total Annotation Level Real or Synthetic AITEX [165] 1 140 105 245 Segmentation mask Real BTAD [107] 3 2,830 Segmentation mask Real DAGM [166] 10 11,500 Segmentation mask Synthetic DEEPPCB [167] 1 1,500 Bounding box Synthetic Eycandies [168] 10 13,250 2,250 15,500 Segmentation mask Synthe…”
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Metadata
- Authors
- Carrera, D., Manganini, F., Boracchi, G., & Lanzarone, E.
- Affiliations
- Politecnico di Milano (IT)
- Istituto di Matematica Applicata e Tecnologie Informatiche (IT)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2017-04
- Volume
- 13
- Issue
- 2
- Pages
- 551-561
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2562062856
- Cited by
- 133
- References
- 61
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 18 · 2023: 20 · 2022: 20 · 2021: 16 · 2020: 15 · 2019: 16 · 2018: 5 · 2017: 3
- FWCI
- 10.86
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Aisberg (University of Bergamo) (submittedVersion)
- Funders
- Consiglio Nazionale delle Ricerche
- Ministry of Education
- University and Research organizations
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- defect detection, nanofibrous materials, scanning electron microscopy images, sparse representation, anomaly detection, electrospinning, smart manufacturing, image patches
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