A Fuzzy Support Vector Machine-Enhanced Convolutional Neural Network for Recognition of Glass Defects

Jin, Yingji; Zhang, Dandan; Li, Maozhen; Wang, Zhaoba; Chen, Youxing

doi:10.1007/s40815-019-00697-9

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Moreover, the largescale neural network used for deep learning requires a lot of computing resources, which also leads to the inevitable large computing cost. For the application of different types of CNN frameworks, the summary is shown in Table Ⅷ: (1) Many studies have shown that CNN is superior to simple machine learning methods [61,63,64]. The traditional shallow CNN has the advantages of less time consumption, light and simple network structure, and low hardware requirements.…”

Section: ⅶ Discussionmentioning

confidence: 99%

“…When using the same data for training, the accuracy of LeNet-5 was 95.97%, and the accuracy of scratchNet was 96.35%. Jin et al [61] proposed a multi-channel self-encoding convolutional network (AECNN) model to deal with the problem of false detection due to small difference in feature space in glass detection. Generally, in order to make the network achieve better information capture ability, it is necessary to increase the number of convolution kernels in the network, which also makes the training time longer.…”

Section: A Application In Mobile Phone Glass Platementioning

confidence: 99%

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Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Ming

Cao

Zhang³

et al. 2021

IEEE Access

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Based on the rapid development of semiconductors, integrated circuits and the Internet. 3C products such as computers, tablets, mobile phones and smart TVs have become an indispensable part of people's lives. With the prosperity and development of the 3C product market, the demand for the quality of display panels and related detection technologies are increasing. As the iconic network of deep learning, has been extensively studied in the field of image recognition and defect detection. Based on the development of CNN, this article summarizes the defect detection method of 3C products by CNN with different depths. First, we reviewed the origin of CNN and its structural components, then introduced the upgrade and improvement of important components, and finally introduced and compared the applications of CNN with different depths in defect detection. Through the comparison and summary of the effect of defect detection, we analyze the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.

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Section: ⅶ Discussionmentioning

confidence: 99%

Section: A Application In Mobile Phone Glass Platementioning

confidence: 99%

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Ming

Cao

Zhang³

et al. 2021

IEEE Access

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“…Since clustering only cares about the width and height of the frames and does not care about their positions, it is assumed that the center points of the two frames coincide when calculating the IOU. Then the IOU of dimension boxes a and B in the watercourse defect data set is shown in equation (2), in which dimension boxes a and B and the intersection and merge ratio are shown in Figure 3. .…”

Section: Improved Anchor Box Generation Scheme Based On K-means++mentioning

confidence: 99%

“…With the continuous development of machine vision technology, defect detection technology based on deep learning is relatively mature in theory and practical application. It has been successfully applied in defect detection of wood [1], glass [2], strip steel [3], battery [4], chip [5], semiconductor [6] and other products, improving the efficiency and accuracy of defect detection. At present, the research of using deep learning technology to detect the water channel defects of engine cylinder block is rare, and there is a problem that the size of some kinds of defects in the water channel is small, which makes it difficult to detect.…”

Section: Introductionmentioning

confidence: 99%

Detection method of water channel defects in engine block based on deep learning

Deng

Li²,

Lin³

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at the low efficiency of manual detection in the detection of water channel defects of engine cylinder block and the poor generalization ability of traditional machine vision in manual design features, an improved water channel defect detection model of cylinder block based on fast RCNN network is proposed. Restnet50 is selected as the feature extraction network of waterway defects, and feature pyramid network (FPN) is introduced to improve the detection ability of small defects; The anchor box is optimized by k-means++ clustering algorithm to improve the positioning of the target box. Experiments show that the map of the improved network in the engine cylinder block waterway defect data set reaches 88.74%, the accuracy is increased by 4% compared with the original fast RCNN, and the recall rate is increased by 1.67%. The recognition and detection effect in real samples is good, and it can be effectively used for the engine cylinder block waterway defect detection.

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“…Recently, with the development of the artificial intelligence field, machine learning is widely applied in the image process [11][12][13], and it has superior performance in defect detection, classification, etc. Yet, it has been rarely reported in defect size recognition.…”

Section: Introductionmentioning

confidence: 99%

Optical Element Surface Defect Size Recognition Based on Decision Regression Tree

Lou

Cao²,

Zhang

et al. 2020

Applied Sciences

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Defect size recognition is significant to the evaluation of optical element surface quality. Currently, it’s mainly achieved by the conventional image process, such as threshold segmentation. However, as the defect size gradually approaches the diffraction limit of the imaging system, the defect gray distribution changes from bimodal to unimodal, which makes it difficult to be accurately recognized. In this paper, an electromagnetic simulation model of the microscopic scattering dark-field imaging (MSDI) system is built based on the finite-difference time-domain (FDTD) method to research the defect imaging mechanism. The point spread function (PSF) of our MSDI system is measured to revise the far-field simulation light intensity distribution, and the mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the light intensity distribution peak value, is taken as the feature parameter of the light intensity distribution. To obtain the defect size, the decision regression tree (DRT) is proposed to get the relationship between the feature parameter and the defect size. Besides, some scratches samples are made to verify the validity of the DRT. The results show the relative error of DRT is within 10%, which is better than the threshold segmentation.

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A Fuzzy Support Vector Machine-Enhanced Convolutional Neural Network for Recognition of Glass Defects

Cited by 11 publications

References 26 publications

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Detection method of water channel defects in engine block based on deep learning

Optical Element Surface Defect Size Recognition Based on Decision Regression Tree

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