Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Ding, Wei; Wang, Qing‐Guo; Zhu, Jianfeng

doi:10.1109/access.2020.2978001

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…The two criteria are the real category and the category predicted by the classification model. The rows of the matrix symbolize the authentic values, and the columns of the matrix describe the predicted values [23,[28][29][30][31]. The confusion matrix analysis is shown in Table 3.…”

Section: Experiments Schemementioning

confidence: 99%

Blood Group Interpretation Algorithm Based on Improved AlexNet

2023

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Traditional blood group interpretation technology has poor detection efficiency and interpretation accuracy in the face of complex conditions in clinical environments. In order to improve the interpretation accuracy of the automatic blood group interpretation system, the important role of deep learning in the blood group interpretation system was studied. Based on the AlexNet network model, this paper proposes an improved scheme because of its advantages in terms of speeding up the convergence training speed and enhancing the model’s generalizability. However, it still needs improvement in terms of blood group interpretation accuracy. The improved AlexNet network model proposed in this paper added an attention mechanism to the network structure, optimized the loss function in the training algorithm, and adjusted the learning rate attenuation function. The experiments showed that compared with the accuracy of the AlexNet model, its training effect was remarkable, with an accuracy of 96.9%—an increase of 3%. Moreover, the improved network model paid more attention to fine-grained classification, minimized the loss rate, and improved the accuracy of system interpretation.

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Section: Experiments Schemementioning

confidence: 99%

Blood Group Interpretation Algorithm Based on Improved AlexNet

2023

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“…The rapid development of machine vision technology has provided a new solution for online defect detection and has achieved certain progress [1][2][3][4][5]. In the field of wafer inspection, scholars have proposed methods for different defect categories.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Ting

et al. 2022

Applied Sciences

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Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.

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“…In the machine vision detection technology, they have high detection accuracy and high stability and are widely used in the field of surface feature measurement and defect detection [11,12]. Zhang et al [13], by analyzing the Si 3 N 4 ceramic balls' defect characteristics, found that some defects cannot be directly detected.…”

Section: Introductionmentioning

confidence: 99%

Si3N4 Ceramic Ball Surface Defects’ Detection Based on SWT and Nonlinear Enhancement

Zhang

et al. 2021

Mathematical Problems in Engineering

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In order to improve the detection accuracy and efficiency of silicon nitride ceramic ball surface defects, a defect detection algorithm based on SWT and nonlinear enhancement is proposed. In view of the small surface defect area and low contrast of the silicon nitride ceramic ball, a machine vision-based nondestructive inspection system for surface images is constructed. Sobel operation is used to eliminate the nonuniform background, and the silicon nitride ceramic ball surface image is decomposed by SWT. And frequency-domain index low-pass filtering is used to modify the decomposition coefficients, and an adaptive nonlinear model is proposed to enhance defects; finally, the image is reconstructed and segmented by the stationary wavelet inverse transform and the dynamic threshold method, respectively. The enhanced algorithm can effectively identify surface defects and is superior to traditional defect detection algorithms.

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Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Cited by 10 publications

References 30 publications

Blood Group Interpretation Algorithm Based on Improved AlexNet

Blood Group Interpretation Algorithm Based on Improved AlexNet

Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Si3N4 Ceramic Ball Surface Defects’ Detection Based on SWT and Nonlinear Enhancement

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