Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Open accessAbstract
Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.
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- Authors
- Napoletano, P., Piccoli, F., & Schettini, R.
- Affiliations
- University of Milano-Bicocca (IT)
- Corresponding author
- Napoletano, Paolo
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-01-12
- Volume
- 18
- Issue
- 1
- Pages
- 209
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5795842
- OpenAlex
- W2784032999
- Cited by
- 285
- References
- 51
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 36 · 2023: 42 · 2022: 58 · 2021: 55 · 2020: 45 · 2019: 17 · 2018: 2
- FWCI
- 24.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, nanofibrous materials, scanning electron microscopy images, self-similarity, convolutional neural networks, visual similarity, defect detection
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