Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | School of Fashion and Textiles | en_US |
| dc.contributor.advisor | Wong, Wai Keung (SFT) | en_US |
| dc.creator | Lyu, Shuai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13828 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | A computer vision model for industrial product surface inspection using unsupervised learning and few-shot learning | en_US |
| dcterms.abstract | The advancement of AI has significantly accelerated CV research in industrial manufacturing, focusing on using AI-based models to detect and classify surface defects in product inspection. However, existing models often underperform in practice due to neglecting defect characteristics. This study addresses three key issues: underutilization of large-scale normal samples, challenges in leveraging few defective samples, and ensuring robust detection against product/inspection standard changes. While industrial settings have abundant normal samples, effectively using them for training remains difficult. Humans can detect/classify defects with minimal examples, but deep learning requires substantial labeled data, which is often unfeasible. Lastly, current models struggle to generalize to new products/standards due to industrial products' vast diversity and irregularity. | en_US |
| dcterms.abstract | First, the unsupervised Reducing Biases (REB) is proposed for industrial anomaly detection representation. This model learns from normal images to detect product surface defects. A self-supervised task fine-tunes the pre-trained model via the DefectMaker strategy, ensuring diverse synthetic defects. Additionally, the local-density k-nearest neighbors (LDKNN) method addresses normal image patterns/distributions, reduces feature space density bias, and enables effective anomaly detection. | en_US |
| dcterms.abstract | Second, this research proposes a novel multi-view region context (MVREC) framework for few-shot defect multi-classification (FSDMC), focusing on generalization and contextual feature extraction. MVREC enhances defect classification by integrating pre-trained AlphaCLIP for general feature extraction and using a region-context framework with multi-view context augmentation. The framework also includes Few-shot Zip-Adapter(-F) classifiers to cache support set features for efficient few-shot classification. To validate MVREC, this study introduces MVTec-FS, a new FSDMC benchmark with instance-level mask annotations for 46 defect categories, and demonstrates its performance via comprehensive experiments on multiple datasets. | en_US |
| dcterms.abstract | Third, addressing fabric inspection challenges in the dynamic clothing industry--where new fabric types and inspection criteria emerge--this research proposes a Continual Learning-based Fabric Inspection Model (CLFIM). Traditional models fail to adapt to unseen patterns or defect categories due to pattern/criteria shifts. CLFIM, trained on specific fabrics, learns pattern and inspection criteria contexts to adapt to new tasks. Experiments on three complex-patterned fabrics with varying criteria show that the YOLOV8-based CLFIM effectively handles evolving inspection challenges. | en_US |
| dcterms.abstract | Overall, this research introduces innovative approaches--REB, MVREC, and CLFIM--to address critical challenges: underutilization of normal/defective samples and detection difficulties for altered products/inspection criteria. These models enhance defect detection/classification across fabric types and industrial products. Incorporating self-supervised, few-shot, and continual learning expands defect detection research scope. | en_US |
| dcterms.extent | ix, 124 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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