A Generic Automated Surface Defect Detection Based on a Bilinear Model

Zhou, Fei; Liu, Guihua; Xu, Feng; Deng, Hao

doi:10.3390/app9153159

Cited by 41 publications

(15 citation statements)

References 26 publications

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“…For the sake of making the model-based methods more universal, Wang et al [ 56 ] and Liu et al [ 36 , 101 ] utilized the guidance information template and proposed methods with preeminent performance. Moreover, Zhou et al [ 102 ] designed a generic method of automated surface defect detection based on a bilinear model, the method realized end-to-end weak monitoring detection of the hot-rolled strips, glass bulb, and other materials by using only small sample data. The model methods are based on the construction model of images and use the statistics of model parameters as texture features.…”

Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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“…The defect regions can be accurately tracked by using a compact CNN. Zhou et al [117] designed a new bilinear model of double-visual geometry group 16 (D-VGG16) to extract global and local features of surface defects, these features were then fed to the gradient-weighted class activation mapping (Grad-CAM) to finish defect detection. The proposed method can simultaneously realize defect classification and localization with small samples in weakly-supervised manner.…”

Section: ) Reinforcement Learningmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

304

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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“…Thus, defects in the original input image can be detected automatically after processing the heat map with a threshold segmentation method. It is possible to perform simultaneously the classification and localization of defects [17] . In order to reduce production times and increase production quality, processes were automated through machine learning (ML) and, in particular, deep learning (DL), algorithms where lot of data are available to extract experience from them.…”

Section: Related Workmentioning

confidence: 99%

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Ieracitano

Paviglianiti

Campolo

et al. 2021

IEEE/CAA J. Autom. Sinica

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The manufacturing of nanomaterials by an electrospinning process requires accurate and meticulous inspection of Scanning Electron Microscope (SEM) images of the electrospun nanofiber, to ensure no structural defects are produced. The possible presence of anomalies is known to make the nanofibrous material useless in the practical application of any nanotechnology. Hence, automatic monitoring and quality control of nanomaterials has become an important challenge in the context of Industry 4.0. In this paper, we propose a novel automatic classification system for homogenous (anomaly-free) and nonhomogenous (with defects) nanofibers avoiding the processing of the redundant full SEM image. Specifically, the image to be analyzed is partitioned into sub-images (nanopatches) that are then used as input to a hybrid unsupervised and supervised machine learning system. An Autoencoder (AE) is first trained with unsupervised learning to generate a code representing the input image with a number of relevant features. Next, a Multilayer Perceptron (MLP), trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber (NH-NF) and homogenous nanofiber (H-NF) materials. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and recent state-of-the-art techniques, reporting accuracy rates up to 92.5%. In addition, our proposed approach achieves significant model complexity reduction with respect to other deep learning strategies such as Convolutional Neural Networks (CNN). The promising performance achieved in this benchmark study will stimulate the application of our proposed framework in a range of challenging industrial manufacturing tasks.

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A Generic Automated Surface Defect Detection Based on a Bilinear Model

Cited by 41 publications

References 26 publications

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Automated Visual Defect Detection for Flat Steel Surface: A Survey

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

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