Defect Detection of Using Variant CNN in the Processing of Cover Glass, Touch Screen and Display under Parallel Light

Wei, Xiangying; Feng, Wei; Lei, Qujiang; Gui, Guangchao; Le, Shangzhi; Zhong, Jiahao

doi:10.1109/iccc51575.2020.9345134

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Experimental results showed that the defect detection rate of the designed CNN could reach 99.5%. Wei et al [18] proposed a multivariable CNN-based defect detection method in the production process of cover glass, touch screen and mobile phone display under parallel light sources. Experiments verified that the method had higher accuracy, better stability and faster speed.…”

Section: B Related Workmentioning

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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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: B Related Workmentioning

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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“…In the task of smartphone screen defect detection, previous defect detection methods face two prevalent challenges: (1) In defect images, small defects lead to an imbalance in the distribution of normal and defective pixels, which causes the network to excessively focus on the background, resulting in insufficient attention to information of defect features.…”

Section: Introductionmentioning

confidence: 99%

“…As a fundamental component of smartphones, the touchscreen has attracted considerable attention regarding its quality. The quality of the touchscreen plays a crucial role in determining the lifespan, user experience and display performance of the smartphone [ 1 ]. The smartphone screen is manufactured from glass material, exhibiting favorable mechanical and optical properties.…”

Section: Introductionmentioning

confidence: 99%

Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection

Liang,

Wang,

2024

Sensors

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Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets.

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Research on ITO Conductive Glass Chipping Defect Detection Method based on Improved Skeleton Extraction

Xiong

Yin

Liu

et al. 2023

2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)

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Defect Detection of Using Variant CNN in the Processing of Cover Glass, Touch Screen and Display under Parallel Light

Cited by 3 publications

References 5 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

Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection

Research on ITO Conductive Glass Chipping Defect Detection Method based on Improved Skeleton Extraction

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