Rethinking unsupervised texture defect detection using PCA

Zhang, NaiXue; Zhong, Yuzhong; Dian, Songyi

doi:10.1016/j.optlaseng.2022.107470

Cited by 8 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Srivastava and Jain [26] combined SMOTE algorithm and K-Means clustering algorithm sequentially to deal with the class imbalance problem and achieved good results. Zhang et al [27] developed a defect identification system based on principal component analysis (PCA) and histogram-based anomaly score (HBOS). The method extracts features using PCA and then fuses them using non-significance suppression.…”

Section: Clustering For Unsupervised Feature Learningmentioning

confidence: 99%

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Bai,

Li,

Jiang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

show abstract

Section: Clustering For Unsupervised Feature Learningmentioning

confidence: 99%

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Bai,

Li,

Jiang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Actually, collecting and annotating numerous defect sample data is challenging and even impractical [7]. Therefore, some scholars [8][9][10] have comprehensively and extensively studied deep-learning-based unsupervised detection methods to detect surface defects. A prevailing method [11,12] is to obtain the reconstruction models with positive product features by learning defect-free samples, then reconstructing defect images into defect-free images by utilizing the trained reconstruction models, and positioning defects by comparing the differences before and after reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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

Tang,

Jin,

Zhang

et al. 2023

Processes

View full text Add to dashboard Cite

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.

show abstract

An integrated defect detection method based on context encoder and perception-enhanced aggregation for cylinder bores

He,

Jin,

Chen

et al. 2024

Journal of Manufacturing Processes

View full text Add to dashboard Cite

Rethinking unsupervised texture defect detection using PCA

Cited by 8 publications

References 20 publications

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

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

An integrated defect detection method based on context encoder and perception-enhanced aggregation for cylinder bores

Contact Info

Product

Resources

About