Fast defect detection for various types of surfaces using random forest with VOV features

Kwon, Bae-Keun; Won, Jong-Seob; Kang, Dong-Joong

doi:10.1007/s12541-015-0125-y

Cited by 45 publications

(16 citation statements)

References 18 publications

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“…Indeed, the wavelet-domain HMT model (Hu et al, 2014) using textile fabric, woven wool, leather and sandpaper surfaces gives the defect detection success rate of (on average) 0.921. The success rate when applying VOV profiles to forest-based machine learning algorithm (Kwon et al, 2015) is 0.927, for wafer, solid car surface, pear colour car surface, paper, fabric, stone and striped-metal.…”

Section: Vaidelienė G Et Al: Haar Wavelets In Detecting Defectsmentioning

confidence: 99%

“…However, at present there is a growing need to develop more flexible defect detection schemes suitable for processing several types of texture surfaces. For instance, Kwon et al (2015) have indicated that seven different classes of texture images can be distinguished using Variance of Variance (VOV) profiles applied to the random forest-based machine learning algorithm. The article (Yuan et al, 2015) describes the modified Otsu method with the weight function which can be used to detect defects on texture surfaces such as wood, fabric, metal, rail images, etc.…”

Section: Introductionmentioning

confidence: 99%

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The Use of Haar Wavelets in Detecting and Localizing Texture Defects

Vaidelienė

Valantinas

2016

Image Anal Stereol

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In this paper, a new Haar wavelet-based approach to the detection and localization of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete Haar wavelet transform (HT) and generates statistically-based parameterized texture defect detection criteria. The criteria provide the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The experiment analyses samples of ceramic tiles, glass samples, as well as fabric scraps, taken from real factory environment.

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Section: Vaidelienė G Et Al: Haar Wavelets In Detecting Defectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

Vaidelienė

Valantinas

2016

Image Anal Stereol

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“…Most proposed defect inspection algorithms for these surfaces are traditional classification or segmentation methods. For traditional classification methods, for example, Gabor [7], LBP (local binary pattern) [8], GLCM (gray-level co-occurrence matrix) [9] and HOG (histogram of oriented gridients) [10] features of the defects are extracted, then the defects are classified by machine learning classifiers, such as SVM (support vector machines) [1], Random Forests [11], AdaBoost [10], and so forth. Traditional segmentation methods would partition…”

Section: Introductionmentioning

confidence: 99%

A Compact Convolutional Neural Network for Surface Defect Inspection

Huang

Qiu

Wang

et al. 2020

Sensors

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The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units). to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units). Our model consists of a light-weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3-4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating-point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspection (ASI). pixels into regions, with the intention to emphasize the regions corresponding to surface defects. For example, Reference [6] firstly preprocesses images using convex optimization, then segments wood defects with Otsu segmentation method. Reference [12] segments fabric defects by the Sobel and the watershed algorithm.These traditional methods involve image preprocessing, feature extraction, feature reduction, and classifier selection, which needs the experience of experts. Besides, most surface defects are tiny and very similar to their background, even the inspection for a single type of defect can be very difficult for these traditional methods. Although traditional algorithms are designed for specific surfaces, effective measures such as recall, precision, and accuracy can barely achieve the standard of industrial applications. This long-existing bottleneck has been restraining the growth of ASI.Convolutional neural networks (CNNs) greatly stimulate the development of ASI [3,13-15] as deep learning methods in computer vision are achieving the state-of-the-art in recent years. As end-to-end solutions, CNNs also simplify the procedures of ASI. For example, a fully convolutional network (FCN) can segment surface defects by supervised learning [16], without any extra pre-process or post-process.A notorious limitation of CNNs is computation. In many cases, expensive GPUs with high computing capabilities are necessary to support large workloads. However, in considering manufacturing cost, most core computing resources of ASI platforms are mainly the low-power CPUs of industrial personal computers (IPC), or even FPGAs (field programmable gate array). To take advantage...

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“…[1][2][3][4][5][6][7][8] As a classical pattern recognition problem, feature extraction (learning) and designing of classifier construct the core. Most of existing works have focused on these two aspects to enhance the classification performance over the past few years.…”

Section: Introductionmentioning

confidence: 99%

Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet

et al. 2017

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An approach to a class-specific and shared dictionary learning (CDSDL) for sparse representation is proposed to classify surface defects of steel sheet. The proposed CDSDL algorithm is modelled as a unified objective function, covering reconstructive error, sparse and discriminative promotion constraints. With the high-quality dictionary, the compact, reconstructive and discriminative feature representation of an image can be extracted. Then the classification can be efficiently performed by discriminative information obtained from the reconstructive error or the sparse vector. Based on a dataset of surface images captured from a practical steel production line, the CDSDL algorithm is carried out to verify its effectiveness. Experimental results indicate that the CDSDL algorithm is more effective in classifying surface defects of steel sheet than other algorithms.KEY WORDS: surface defects of steel sheet; image classification; sparse representation; discriminative dictionary learning.

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Fast defect detection for various types of surfaces using random forest with VOV features

Cited by 45 publications

References 18 publications

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

A Compact Convolutional Neural Network for Surface Defect Inspection

Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet

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