Defect Sample Generation System Based on DCGAN for Glass Package Electrical Connectors

Liu, Qunpo; Wang, Mengke; Hanajima, Naohiko

doi:10.1007/978-981-15-8458-9_46

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Before the experiment, two parts of data were screened from the original data set [ 18 ], one part of the data was used as the training set and the other part was used as the test set, and the two sets of data sets were recorded as data1 and data2, respectively. Similarly, the animation face dataset is also processed in the same way [ 19 ], and at the same time, all images used in the experiment are guaranteed to be 515 × 512 in size.…”

Section: Experimental Research On Facial Expression Generation Method...mentioning

confidence: 99%

3D Animation Automatic Generation System Design Based on Deep Learning

Cao

Wan

Shi

2022

Computational Intelligence and Neuroscience

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In the field of 3D animation design and generation, the expression generation method of animation is not obvious due to the lack of image details, which leads to the lack of realism of the generated animation expressions. In order to solve this problem, a deep learning-based animation character expression generation method is proposed. The method, based on the real facial expression images, uses improved deep learning to design cascade classifiers, extracts facial expression feature images from real images, softens image edges, and enhances feature details. The content and style of images are unified, the loss function is designed from the content constraints and style constraints, the judgment network is optimized, and the feature information is fused under the constraints of the loss function to generate the facial expressions of animated characters. The experimental results show that the design based on the feature point location of the improved deep learning expression generation method is accurate, the Pearson correlation coefficient between the input image and the generated image is high, the root mean square error is small, and the realism of the generated facial expression is enhanced.

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Section: Experimental Research On Facial Expression Generation Method...mentioning

confidence: 99%

3D Animation Automatic Generation System Design Based on Deep Learning

Cao

Wan

Shi

2022

Computational Intelligence and Neuroscience

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“…Due to the limitation of production level and detection methods, some produced glass terminals have defects such as missing blocks, pores, and cracks (Figure 1). The difficulties in defect detection are mainly three points [2]: (1) The complex imaging background of the defects contains a variety of interference noise; (2) The shape, size, and location of defects are diverse; (3) Due to the different locations, sizes and shapes of missing blocks or pore defects, various defects will show greater differences. Therefore, this paper proposes to use deep learning technology to detect missing blocks [3].…”

Section: Introductionmentioning

confidence: 99%

Defect Detection of Micro-Precision Glass Insulated Terminals

Liu¹,

Wang²,

Liu³

et al. 2021

JRNAL

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Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.

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Defect Sample Generation System Based on DCGAN for Glass Package Electrical Connectors

Cited by 2 publications

References 8 publications

3D Animation Automatic Generation System Design Based on Deep Learning

3D Animation Automatic Generation System Design Based on Deep Learning

Defect Detection of Micro-Precision Glass Insulated Terminals

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