ScratchNet: Detecting the Scratches on Cellphone Screen

Luo, Zhao; Xiao, Xiaobing; Ge, Shiming; Ye, Qiting; Zhao, Shengwei; Jin, Xin

doi:10.1007/978-981-10-7305-2_16

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…The CNN model, as one of the most important classification methods, was initially introduced by Yann LeCun et al in 1998 [ 32 ]. Over time, it was refined for the development of deep-learning architectures [ 33 ]. Weimer et al [ 34 ] proposed a classification approach for six distinct panel defects and fine-tuned the CNN hyperparameters, such as the quantity of kernels in each layer and the number of filters in each layer within the CNN model, to enhance classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction

Lin,

Lai,

2024

Sensors

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A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels’ low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction

Lin,

Lai,

2024

Sensors

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“…In our previous work [4], the Gabor filter-based approaches were proposed, which uses prior knowledge to detect ambiguous scratch. Similarly, Luo et al [5] proposed an automated scratches detection module. This automated scratch module first filters out the significant scratches and focus on the small scratches.…”

Section: Introductionmentioning

confidence: 99%

Weak Scratch Detection of Optical Components Using Attention Fusion Network

Tao

Zhang

Singh

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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Scratches on the optical surface can directly affect the reliability of the optical system. Machine vision-based methods have been widely applied in various industrial surface defect inspection scenarios. Since weak scratches imaging in the dark field has an ambiguous edge and low contrast, which brings difficulty in automatic defect detection. To address the problems arising from industry-specific characteristics, this paper proposes "Attention Fusion Network", a convolutional neural network using attention mechanism built by hard and soft attention modules to generate attention-aware features. The hard attention module is implemented by integrating the brightness adjustment operation in the network, and the soft attention module is composed of scale attention and channel attention. The proposed model is trained on a real-world industrial scratch dataset and compared with state-of-art defect inspection methods. The proposed method can achieve the best performance to detect the weak scratch inspection of optical components compared to the state-of-art.

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A comprehensive review of defect detection in 3C glass components

Ming

Shen

et al. 2020

Measurement

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ScratchNet: Detecting the Scratches on Cellphone Screen

Cited by 8 publications

References 15 publications

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction

Weak Scratch Detection of Optical Components Using Attention Fusion Network

A comprehensive review of defect detection in 3C glass components

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