Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
Open accessAbstract
Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.
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The performance of the detection software was measured based on detection rates from the confusion matrix, with ground truth determined by an inspection expert.
“The performance of the proposed software was measured based on the detection rates from the confusion matrix, which is a commonly used evaluation method for defect detection applications [90]. The ground truth was determined by an inspection expert and formed the basis of comparison between the computed and actual detections.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [90]
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- Authors
- Mei, S., Wang, Y., & Wen, G.
- Affiliations
- China University of Geosciences (CN)
- Corresponding author
- Wen, Guojun
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-04-02
- Volume
- 18
- Issue
- 4
- Pages
- 1064
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5948749
- OpenAlex
- W2795647708
- Cited by
- 284
- References
- 37
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 37 · 2023: 35 · 2022: 55 · 2021: 52 · 2020: 38 · 2019: 24 · 2018: 5 · 2017: 1
- 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)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- fabric defect detection, convolutional denoising autoencoder, unsupervised learning, textile quality control, defect localization, reconstruction residuals, multimodal integration
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