Development of an optical inspection platform for surface defect detection in touch panel glass

Chang, Ming; Chen, Bo-Cheng; Gabayno, Jacque Lynn; Chen, Ming-Fu

doi:10.1080/15599612.2016.1166304

Cited by 33 publications

(23 citation statements)

References 8 publications

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“…The present work improves on the AOI apparatus reported in Chang et al [6] in terms of simultaneous execution of defect detection and classification. The detection is based on the principle that the presence of defects on the specimen will scatter light toward the detection plane of a linear CCD camera.…”

Section: Introductionmentioning

confidence: 58%

“…In this study, the construction of AOI equipment is shown in Figure 1, where both the imaging module and specimen stage are controlled automatically by a software package custom developed for the AOI system for image processing and hardware controls. [6] The imaging module consists of 12,288 pixel-line CCD camera (Basler raL12288-66 km) with 3.5 µm per pixel resolution and lens assembly. The illumination is provided by an light emitting diode (LED) array positioned a few centimeters above the specimen stage.…”

Section: Optical Inspection Frameworkmentioning

confidence: 99%

“…Since the light reflected from the transparent sample is very weak, 10 mm was selected to inspect the surface of the touch panel Central moments of order (p + q) of 2D image glass. [6] Light scattered from the surface of the specimen due to surface defects is collected by the line CCD. If the surface is perfectly smooth, which is an indicator of a flawless sample, the recorded image is mostly dark.…”

Section: Optical Inspection Frameworkmentioning

confidence: 99%

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High-resolution optical inspection system for fast detection and classification of surface defects

Chang

Pan

et al. 2018

International Journal of Optomechatronics

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A high-resolution automated optical inspection (AOI) system based on parallel computing is developed to achieve fast inspection and classification of surface defects. To perform fast inspection, the AOI apparatus is connected to a central computer which executes image processing instructions in a graphical processing unit. Defect classification is simultaneously implemented with Hu's moment invariants and back propagation neural (BPN) approach. Experiments on touch panel glass show that using 100 training samples and 1000 cycle iterations in BPN, the accurate classification of surface defects for a 350 � 350 pixels image can be completed in less than 0.1 ms. Moreover, the inspection of a 43 mm � 229 mm sample that yields an 800 megapixel raw data can be completed remarkably fast in less than 3 s. Thus, the AOI system is capable of performing fast, reliable, and fully integrated inspection and classification equipment for in-line measurements. KEYWORDS Back propagation neural; defect classification; defect inspection; image moment invariants; parallel computing Recently, Chang et al. [6] developed an AOI technology based on a high-resolution linear charge-coupled device (CCD) to implement surface defect inspection over wide area samples. At 3.5 µm per pixel resolution of the linear CCD, there were at least 147 megapixel image data per second transmitted from a host CPU to a graphical processing unit (GPU) device for image none defined CONTACT Ming Chang

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Section: Introductionmentioning

confidence: 58%

Section: Optical Inspection Frameworkmentioning

confidence: 99%

Section: Optical Inspection Frameworkmentioning

confidence: 99%

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High-resolution optical inspection system for fast detection and classification of surface defects

Chang

Pan

et al. 2018

International Journal of Optomechatronics

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“…Figure 22(a) shows the difference in detecting the defects using conventional light source and striped light source. Chang et al in [288] offered an AOI system to filter and classify touch panel glass defects. Their illumination setting consisted of two LED lights with fan-shaped light transmission.…”

Section: A Contrast and Illumination Settingsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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“…In addition, Ming Chang et al [ 11 ] presented the development of an optical inspection platform that combines parallel image processing with high resolution opto-mechanical module for the detection of defects of touch panel glass. This work proposes the implementation of a custom illumination source to improve the scattering efficiency of the surface, while improving the contrast of the obtained images and the performance of the vision inspection system.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Papacharalampopoulos

Tzimanis

Sabatakakis

et al. 2020

Sensors

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Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.

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Development of an optical inspection platform for surface defect detection in touch panel glass

Cited by 33 publications

References 8 publications

High-resolution optical inspection system for fast detection and classification of surface defects

High-resolution optical inspection system for fast detection and classification of surface defects

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

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