A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects

• Published in: Processes Vol. 11; no. 9; p. 2615
• Main Authors: Tang, Shancheng, Jin, Zicheng, Zhang, Ying, Lu, Jianhui, Li, Heng, Yang, Jiqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Accuracy; Blurring; Defects; Diffusion; Image quality; Image reconstruction; Iterative methods; Labor costs; Methods; Optimization; Probabilistic models; Production methods; Quality control; Statistical analysis; Surface defects; Vision systems
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr11092615

Defect detection is crucial in quality control for fabric production. Deep-learning-based unsupervised reconstruction methods have been recognized universally to address the scarcity of fabric defect samples, high costs of labeling, and insufficient prior knowledge. However, these methods are subject to several weaknesses in reconstructing defect images into defect-free images with high quality, like image blurring, defect residue, and texture inconsistency, resulting in false detection and missed detection. Therefore, this article proposes an unsupervised detection method for fabric surface defects oriented to the timestep adaptive diffusion model. Firstly, the Simplex Noise–Denoising Diffusion Probabilistic Model (SN-DDPM) is constructed to recursively optimize the distribution of the posterior latent vector, thus gradually approaching the probability distribution of surface features of the defect-free samples through multiple iterative diffusions. Meanwhile, the timestep adaptive module is utilized to dynamically adjust the optimal timestep, enabling the model to flexibly adapt to different data distributions. During the detection, the SN-DDPM is employed to reconstruct the defect images into defect-free images, and image differentiation, frequency-tuned salient detection (FTSD), and threshold binarization are utilized to segment the defects. The results reveal that compared with the other seven unsupervised detection methods, the proposed method exhibits higher F1 and IoU values, which are increased by at least 5.42% and 7.61%, respectively, demonstrating that the proposed method is effective and accurate.