Visual inspection of steel surface defects based on domain adaptation and adaptive convolutional neural network

Zhang, Siyu; Zhang, Qiuju; Gu, Jiefei; Su, Lei; Li, Ké; Pecht, Michael

doi:10.1016/j.ymssp.2020.107541

Cited by 49 publications

(12 citation statements)

References 35 publications

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“…Has a significant impact. 15,16 The current detection and segmentation networks basically extract the features of the target through layer-by-layer abstraction. The process of deepening the number of layers of CNN is the process of feature extraction from low-level to high-level semantic feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

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The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down-sampling of low-level features and up-sampling of deconvolution of high-level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small-scale target.

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Section: Related Workmentioning

confidence: 99%

Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

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“…Unsupervised and semi-supervised learning can also reduce the demand for massive defect samples [33] [135,142]. In addition, for small samples, the method of transfer learning can also be used to detect steel surface defects [173].…”

Section: Discussion and Summarymentioning

confidence: 99%

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

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Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

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“…They compared the calculation results with the models developed by the logistic regression method and the SVM method. The results of modelling, aimed at detecting defects in the surface of products using deep neural networks, can be found, among others, in References [82][83][84][85][86][87].…”

Section: Deep Neural Networkmentioning

confidence: 99%

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek

Trzaska

2021

Metals

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Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

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Visual inspection of steel surface defects based on domain adaptation and adaptive convolutional neural network

Cited by 49 publications

References 35 publications

Improved single shot multibox detector target detection method based on deep feature fusion

Improved single shot multibox detector target detection method based on deep feature fusion

Review of surface defect detection of steel products based on machine vision

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

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