Fabric Defect Detection Based on Multiscale Normalizing Flow Model

doi:10.3901/JME.2025.10.241

Abstract

Abstract: Deep learning-based fabric defect detection methods rely heavily on a substantial collection of labeled defect samples, which can be challenging to obtain and annotate during actual manufacturing processes. To address this issue, a fabric defect detection method based on multiscale normalizing flow model is proposed. The method initially extracts features from defect-free samples via a feature extraction network with pre-trained weights. Subsequently, it establishes a mapping relationship between the feature distribution of defect-free samples and the standard Gaussian distribution through training the normalizing flow model. This mapping enables the assessment of the probability density of a test image’s features within the standard Gaussian distribution to identify and localize defects. In order to mitigate the loss of texture details caused by the low resolution of deep features, a fusion strategy is proposed to fuse the feature distributions at different scales. During the detection process, the probability density of the features for the fabric image is calculated to evaluate the anomaly degree of each pixel, and defect regions are delineated based on a predefined threshold. Experimental results demonstrate that the proposed multiscale flow model-based detection approach can accomplish fabric defect detection without requiring defect samples, achieving pixel-wise accuracy. It exhibits outstanding performance on fabric defect dataset with varying texture backgrounds and defects, achieving a 100% image-wise AUC metric and a 98.5% pixel-wise AUC metric, showcasing robust generalization capabilities.

Key words: fabric defect detection, defect-free samples, normalizing flow, multiscale, deep learning

CLC Number:

TS101

YU Zhiqi, XU Yang, WANG Yuanfei, SHENG Xiaowei. Fabric Defect Detection Based on Multiscale Normalizing Flow Model[J]. Journal of Mechanical Engineering, 2025, 61(10): 241-249.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.10.241

http://www.cjmenet.com.cn/EN/Y2025/V61/I10/241

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