Surface defect classification of steels with a new semi-supervised learning method

He, Di; Xu, Ke; Zhou, Peng; Dongdong, Zhou

doi:10.1016/j.optlaseng.2019.01.011

Cited by 177 publications

(73 citation statements)

References 10 publications

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“…Based on their processing mechanics, these classifiers can be classified in two groups: (1) supervised; and (2) non-supervised or semi-supervised classifiers (see Table 3). [12,13,21,30,31,38,142,148,149,171,[192][193][194][195] Unsupervised/semi-Statistical/Novelty detection [58,65,86,[89][90][91][92]103,115,129,[196][197][198][199][200][201][202] supervised classifiers Gaussian mixture model [80,[203][204][205] Supervised classification methods incorporate the human model-as discussed in Section 3-where the application is searching for features of a predefined class. Detectable features are predefined and the classifier has to be previously trained to recognize them under supervision [40,65,90,103,142,[161][162][163].…”

Section: Supervised and Non-supervised Classifiersmentioning

confidence: 99%

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Czimmermann

Ciuti

Milazzo

et al. 2020

Sensors

226

100

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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

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Section: Supervised and Non-supervised Classifiersmentioning

confidence: 99%

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Czimmermann

Ciuti

Milazzo

et al. 2020

Sensors

226

100

View full text Add to dashboard Cite

show abstract

“…Despite the varying size or scale of images, the algorithm is still able to detect the defects on concrete surfaces with high efficiency. He et al [ 5 ] developed a semi-supervised learning model combining a convolutional autoencoder and a semi-supervised generative adversarial network to detect steel surface defects. The model obtained a 16% improvement compared with the traditional method for hot-rolled plates detection.…”

Section: Related Workmentioning

confidence: 99%

“…With the recent advancement in deep learning, surface defect classification with deep learning-based techniques has become popular. For example, the works of [ 3 , 4 , 5 ] utilize deep learning models for surface defects classification which proves that deep learning models are far more accurate than traditional image processing-based and machine learning methods. To enhance the performance, data augmentation methods are usually utilized [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

et al. 2020

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Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost.

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“…Beijing University of Science and Technology has adopted the deep learning methods to detect steel surface, which is vital to the steel product. With the help of deep learning method, the detection precision of hot rolled plates has been improved around 16% than traditional methods [47]. In [48], a deep learning algorithm was used to detect breaches, dents, burrs and abrasions on the sealing surface of a container.…”

Section: Inspection Technologiesmentioning

confidence: 99%

Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends

Yang

Ren

et al. 2019

2019 25th International Conference on Automation and Computing (ICAC)

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With the increasing demands for energy, oil and gas companies have a demand to improve their efficiency, productivity and safety. Any potential corrosions and cracks on their production, storage or transportation facilities could cause disasters to both human society and the natural environment. Since many oil and gas assets are located in the extreme environment, there is an ongoing demand for robots to perform inspection tasks, which will be more cost-effective and safer. This paper provides a state of art review of inspection robots used in the oil and gas industry which including remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). Different kinds of inspection robots are designed for inspecting different asset structures. The outcome of the review suggests that the reliable autonomous inspection UAVs and AUVs will gain interest among these robots and reliable autonomous localisation, environment mapping, intelligent control strategies, path planning and Non-Destructive Testing (NDT) technology will be the primary areas of research.

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Surface defect classification of steels with a new semi-supervised learning method

Cited by 177 publications

References 10 publications

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends

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