Deep learning based defect inspetion in TFT-LCD rib depth detection

Ho, Chao-Ching; Wang, Hao-Ping; Chiao, Yuan-Cheng

doi:10.1016/j.measen.2021.100198

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Ho et al [13] have introduced a system for detecting the depth of rib marks on TFT-LCDs, comprising three key components likes hardware, system control, software. In hardware section, utilized line scan camera paired by telecentric coaxial lens for imaging.…”

Section: Related Workmentioning

confidence: 99%

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

Xiujun Du

2024

jes

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In smart meter technology, the reliability of Backplane to Bezel (BTB) Liquid Crystal Displays (LCDs) is crucial for efficient functioning within intelligent grid systems. Defects in these LCD screens can significantly impact overall smart meter performance, necessitating effective detection methods for proper management and utilization. Current detection approaches, which combine manual and automatic methods based on machine vision, have shown unsatisfactory performance. This research addresses this challenge by proposing a novel method for advanced fault detection in BTB LCDs of smart meters, Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for smart machine (BTB-LCD-FD- MCDSGAN) is proposed. The study begins by collecting datasets specifically tailored for LCD screen localization and defect detection. To enhance data quality, a Window Adaptive Extended Kalman Filter (WAEKF) is applied during preprocessing for noise removal. Feature extraction follows, utilizing Parameterized Multi Synchrosqueezing Transforms (PMST) with a primary focus on Gray Level Co-occurrence Matrix features. These extracted features serve as input for classification by a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN), categorizing defects into five types likes normal display, no display, abnormal display, liquid crystal rupture, and incomplete display. Furthermore, the proposed method optimizes the MCDSGAN weight parameter using the Red Fox Optimization Algorithm (RFOA) to achieve accurate LCD fault defect prediction. The entire approach is implemented in Python, performance metrics likes accuracy, precision, recall, F-score, computational time are thoroughly analyzed. Performance of proposed BTB-LCD-FD-MCDSGAN approach attains 20.89%, 33.67% and 25.98% high accuracy, and 17.98%, 23.78% and 33.45% higher recall compared with existing methods such as automatic detection of display defects for smart meters depend on deep learning (AD-AM-DL), deep learning-enabled image content-adaptive field sequential color LCDs by mini-LED backlight (DL-AFSC-LCD) and new multi category defect detection method depend on convolutional neural network technique for TFT-LCD panels (MDF-CNN-LCD), methods respectively.

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Section: Related Workmentioning

confidence: 99%

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

Xiujun Du

2024

jes

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“…This proposes a collective training mechanism for defect prediction (CTDP), which makes the feature distributions of source and target items similar to each other through transfer learning, and comprehensively considers multiple source items to predict the target. Recently, deep-learning-based AOI inspection system have been emerging for manufacturers, e.g., eyeglass [9], mobile phone back glass [10], fabric [11], TFT-LCD [12], and wafer classification [13]. In 2022, our previous work [14] showed an AOI solution with a deep residual neural network for defect detection over complex backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Surface Texture Feature Simulation for Surface Defect Inspection

Tai

2022

Symmetry

Self Cite

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In this research, a simulation system based on a physical model and its lighting feature is developed to perform three-dimensional model creation, and graphics software is used to randomly generate a simulated surface with defects, which also cooperates with the virtual environment to reproduce the original environment. Furthermore, the use of a generative adversarial network to optimize the virtual dataset created symmetrically by the system is studied in order to reduce the effect of the difference between the real and virtual images. This system compensates for the condition of data imbalance occurring between qualified products and defective products in the production line, and a large amount of random data with and without defects can be created. In addition, the process of the database creation is classified and marked, such that complicated and time-consuming preliminary steps can be reduced; therefore, the data collection cost can be significantly reduced and the uncertainly associated with manual operation is also reduced. When a simulated textured surface generated from this system is used to perform training, the inspection background accuracy reaches 98%, and the accuracy also reaches 78% in real defect inspection process; therefore, the location of the defect can be determined completely.

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A smart Warehouse 4.0 approach for the pallet management using machine vision and Internet of Things (IoT): A real industrial case study

Vukićević¹,

Mladineo²,

Banduka³

et al. 2021

Adv produc engineer manag

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Printing companies are commonly SMEs with high flow of materials, which management could be significantly improved through the digitalization. In this study we propose a smart Warehouse 4.0 solution by using QR code, open-source software tools for machine vision and conventional surveillance equipment. Although there have been concerns regarding the usage of QR in logistics, it has shown to be suitable for the particular use-case as pallets are static in the interwarehouse. The reliability of reading of QR codes was achieved by using multiple IP cameras, so that sub-optimal view angle or light reflection is compensated with alternative views. Since surveillance technology and machine vision are constantly evolving and becoming more affordable, we report that more attention needs to be invested into their adaptation to fit the needs and budgets of SMEs, which are the industrial cornerstone in the most developed countries. The demo of proposed solution is available on the public repository https://github.com/ArsoVukicevic/PalletManagement/.

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Deep learning based defect inspetion in TFT-LCD rib depth detection

Cited by 3 publications

References 3 publications

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

Deep-Learning-Based Surface Texture Feature Simulation for Surface Defect Inspection

A smart Warehouse 4.0 approach for the pallet management using machine vision and Internet of Things (IoT): A real industrial case study

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