Using ISU-GAN for unsupervised small sample defect detection

Yijing, Guo; Zhong, Linwei; Qiu, Yi; Wang, Huawei; Gao, Fengqiang; Wen, Zongheng; Zhan, Choujun

doi:10.1038/s41598-022-15855-7

Cited by 7 publications

(5 citation statements)

References 33 publications

(20 reference statements)

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“…Jin Rui et al [15] proposed a fabric-defect-detection method based on an improved generative adversarial network, introducing a center loss constraint to improve the recognition performance of the method, which was evaluated on the publicly available Tianchi dataset with good results. Yijing Guo et al [16] proposed a new unsupervised small-sample-defect-detection model based on the DAGM2007 dataset that performs well with a small number of training samples. Although the above methods are more effective on specific detection targets, their reconstruction networks are difficult to accurately reconstruct irregularly distributed images, and their detection results are susceptible to various factors such as the color, size, and illumination of the detection target.…”

Section: Related Work 21 Deep Learning Detection Methodsmentioning

confidence: 99%

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

Tang,

Zhang,

Jin

et al. 2023

Applied Sciences

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The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the aluminum profile surface presents non-uniform and non-periodic features, and this irregular distribution makes it difficult for classical reconstruction networks to accurately reconstruct the normal features, resulting in low performance of related unsupervised detection methods. Aiming at such problems, a feature-oriented reconstruction method of unsupervised surface-defect detection method for aluminum profiles is proposed. The aluminum profile image preprocessing stage uses techniques such as boundary extraction, background removal, and data normalization to process the original image and extract the image of the main part of the aluminum profile, which reduces the influence of irrelevant data features on the algorithm. The essential features learning stage precedes the feature-optimization module to eliminate the texture interference of the irregular distribution of the aluminum profile surface, and image blocks of the area images are reconstructed one by one to extract the features through the mask. The defect-detection stage compares the structural similarity of the feature images before and after the reconstruction, and comprehensively determines the detection results. The experimental results improve detection precision by 1.4% and the F1 value by 1.2% over the existing unsupervised methods, proving the effectiveness and superiority of the proposed method.

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Section: Related Work 21 Deep Learning Detection Methodsmentioning

confidence: 99%

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

Tang,

Zhang,

Jin

et al. 2023

Applied Sciences

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“…In summary, recent years have witnessed significant progress in research on surface defect detection algorithms for wood panels, driven by deep learning methods. In addition, when faced with the task of detecting defects with a small number of defect samples, researchers have begun to explore unsupervised learning-based defect detection methods and have achieved many promising results [31][32][33]. However, for defects characterized by small size, complex textures, and low feature recognition, current deep learning detection methods still struggle to ensure high efficiency at the application level, resulting in issues such as misrecognition, omissions, and reduced stability.…”

Section: Related Workmentioning

confidence: 99%

An efficient and accurate surface defect detection method for quality supervision of wood panels

Yi,

Luo,

et al. 2024

Meas. Sci. Technol.

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The wood panel processing sector is integral to the landscape of industrial manufacturing, and automated detection of wood panel surface defects has become an important guarantee for improving the efficiency and quality of processing production. However, due to the diverse scales and shapes of wood panel surface defects, as well as their complex and varied colors and texture characteristics, the efforts to efficiently and accurately detect surface defects in wood panels through existing methods have fallen short. Therefore, the paper proposes an enhanced YOLOx-tiny deep learning network for wood panel surface defect detection. We introduce new modules MFFM and CFEM, instead of the original SPP and Bottleneck modules to enhance key feature extraction and reduce the number of computational parameters.The experimental results conducted on the self-constructed wood panel surface defects dataset show that the mAP of our proposed method is 95.01%, which is 9.58% higher than the original YOLOx-tiny network model, and the defects recall is 91.46%, which is 13.21% higher compared to the original network. Meanwhile, the method is able to reduce 12.22% of computational parameters and 12.67% of GFLOPs, which effectively improves the efficiency of the detection of surface defects on wood panels. In summary, the proposed intelligent surface defect detection approach for wood panels, which utilizes an enhanced YOLOx-tiny deep learning network, has yielded notable outcomes in enhancing both accuracy and efficiency. This method holds significant practical relevance for the wood panel manufacturing sector, offering the potential to enhance both production efficiency and quality. It also explores the automation and intelligent technology in the process of man-made board processing, which provides a valuable reference for the research in related fields.

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“…Despite the significant advantages of deep learning in surface defect detection, its training requires a large number of defect image samples 27 . The limited quantity of defect samples in practical production has always been a challenge in using deep learning models for defect detection 28 . Therefore, it is necessary to design a defect generation method that meets the task requirements to address issues such as imbalanced and insufficient defect data, lack of diversity, and poor quality.…”

Section: Related Workmentioning

confidence: 99%

DG2GAN: improving defect recognition performance with generated defect image sample

Deng,

Luo,

et al. 2024

Sci Rep

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This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image data for product defect recognition. A novel defect generation method with multiple loss functions, DG2GAN is presented in this paper. This method employs cycle consistency loss to generate defect images from a large number of defect-free images, overcoming the issue of imbalanced original training data. DJS optimized discriminator loss is introduced in the added discriminator to encourage the generation of diverse defect images. Furthermore, to maintain diversity in generated images while improving image quality, a new DG2 adversarial loss is proposed with the aim of generating high-quality and diverse images. The experiments demonstrated that DG2GAN produces defect images of higher quality and greater diversity compared with other advanced generation methods. Using the DG2GAN method to augment defect data in the CrackForest and MVTec datasets, the defect recognition accuracy increased from 86.9 to 94.6%, and the precision improved from 59.8 to 80.2%. The experimental results show that using the proposed defect generation method can obtain sample images with high quality and diversity and employ this method for data augmentation significantly enhances surface defect recognition technology.

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Using ISU-GAN for unsupervised small sample defect detection

Cited by 7 publications

References 33 publications

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

An efficient and accurate surface defect detection method for quality supervision of wood panels

DG2GAN: improving defect recognition performance with generated defect image sample

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