Surface Defect Modelling Using Co-occurrence Matrix and Fast Fourier Transformation

Pastor-López, Iker; Sanz, Borja; Puerta, José Gaviria de la; Bringas, Pablo García

doi:10.1007/978-3-030-29859-3_63

Cited by 8 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Filter-based methods apply some filter banks on defect images, and calculate the energy of the filter responses [35][36][37][38][39][40][41][42][43][44][45][46][47]. Common filter-based methods include Sobel operator, Canny operator, Gabor operator, Laplacian operator, wavelet transform, and Fourier transform, which can be further divided into spatial domain, frequency domain, and spatial-frequency domain methods.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

View full text Add to dashboard Cite

In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

show abstract

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For defect detection in steel surface images, traditional ML techniques have shown reliable results in many cases [17]. Due to the small size of datasets, Iker et al, have used traditional ML techniques which are k-Nearest Neighbors (kNN), Bayesian networks, Support Vector Machines (SVM) and decision trees for defects classification of surface of iron casting images [21]. For feature extraction from the images, the authors have used Fast Fourier Transformation (FFT) and Co-occurrence Matrix methods.…”

Section: Related Workmentioning

confidence: 99%

Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case

Dasari

Cheddad

Palmquist

2020

2020 7th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

View full text Add to dashboard Cite

One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for defects classification to speed up the manual verification criteria since a huge data is generated from the additive manufacturing. Although a huge image data is available, the data needs to be labelled manually by experts which results in small sample datasets. Hence, to model small sample sizes and also to acquire the importance of parameters, we opted a traditional machine learning method, Random Forests (RF). For feature extraction, we opted for the Polar Transformation to explore its applicability using the meltpool image dataset and a publicly available shape image dataset. The results show that RF models with Polar Transformation performed the best on our case study datasets and the second-best for the public dataset when compared to the Histogram of Oriented Gradients, HARALICK, XY-projections of an image, and Local Binary Patterns methods. As such, the Polar Transformation can be considered as a suitable compact shape descriptor.

show abstract

“…Design feature-based methods include statistical methods (histograms [3], cooccurrence matrices [4], local binary patterns [5]), structural methods (morphology [6]), filtering methods [7,8] (Canny, Gabor, FFT, Wavelet, Sobel, etc. ), model-based methods [9], and multi-feature combination methods [10]. However, design feature-based methods often require professionals to tailor them for specific tasks, making them unsuitable for transfer applications and with limited resistance to interference.…”

Section: Introductionmentioning

confidence: 99%

Efficient Defect Classification Using Few-Shot Image Generation and Self-Attention Fused Convolution Features

Zhang,

Yang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines.

show abstract

Surface Defect Modelling Using Co-occurrence Matrix and Fast Fourier Transformation

Cited by 8 publications

References 20 publications

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case

Efficient Defect Classification Using Few-Shot Image Generation and Self-Attention Fused Convolution Features

Contact Info

Product

Resources

About