Tiny surface defect inspection of electronic passive components using discrete cosine transform decomposition and cumulative sum techniques

Lin, Hong-Dar

doi:10.1016/j.imavis.2007.07.009

Cited by 29 publications

(14 citation statements)

References 32 publications

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“…Lin and Jiang [10] combined discrete cosine transform and gray relational analysis technique to inspect surface defects on encapsulations of lightemitting diodes (LEDs). Also, Lin [9] further developed a novel approach that applies discrete cosine transform decomposition and cumulative sum techniques for the detection of tiny defects on passive component chips.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is difficult to precisely detect surface defects imbedded in the complicated directional textures. Therefore, most of the existing research works focus on inspections of textile fabrics [1,4,6,7], machined surfaces [18,19], natural wood [18,19], electronic components [8][9][10]15], and RTPs [2]. They do not detect defects with the properties of small defects on CTP surfaces of glass products.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automated quality inspection of surface defects on touch panels

Lin

Tsai

2012

Journal of the Chinese Institute of Industrial Engineers

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Capacitive touch panels (CTPs) with advantages of water-proof, stain-proof, scratch-proof, and fast response are widely used in various electronic products built in touch technology functions. It is a difficult inspection task when defects imbedded on surfaces of CTPs with structural textures. This research proposes a Fourier transform-based approach to inspect surface defects of CTPs. When a CTP image with four directional and periodic lines of texture is transformed to Fourier domain, four principal bands with high-energy frequency components crisscross at the center of Fourier spectrum. A multi-crisscross filter is designed to filter out the frequency components of the principal band regions. The filtered image is then transformed back to spatial domain. Finally, the restored image is segmented by a simple threshold method and defects are located. Experimental results show the proposed method achieves a high defect detection rate and a low false alarm rate on defect inspection of touch panels.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Automated quality inspection of surface defects on touch panels

Lin

Tsai

2012

Journal of the Chinese Institute of Industrial Engineers

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“…To verify the performance of the proposed MSCDAE model more fully, in this section, we will compare the inspection results of this model with those of several unsupervised algorithms, specifically the discrete cosine transform (DCT) [ 40 ], the phase only transform (PHOT) [ 10 ] and the nonlocal sparse representation (NLSR) [ 41 ] approaches. The DCT model is a typical defect inspection method that tries to eliminate random texture patterns and retain anomalies in the restored image by reconstructing the frequency matrix without selected large-magnitude frequency values.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

237

121

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Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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“…Minor defects were also included in separate category. [290] IR-CUT filter defects such as stain, scratch, and edge crack [296] Surface defects in micro multi-layer nonspherical lens module of CMOS such as bright spot, dark spot, scratch, foreign material and hole [297] Compact camera lens and spacer ring defects such as stain, bright dot, scratch, pit, and scar Passive Components [291], [292] Ripple defects in the surface barrier Layer chips of ceramic capacitors [298] Tiny surface defects in the surface barrier Layer chips of passive electronic components [299] Surface defects of film capacitors Thermal Fuse [293] Bur, black dot, small-head, and flake defects and lightning setup, computer (processor), conveyor and sorting mechanism as shown in Figure 16. The illumination is responsible for providing constant/customized lightning conditions.…”

Section: Image Acquisition Technologies -Hardware Systemsmentioning

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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Tiny surface defect inspection of electronic passive components using discrete cosine transform decomposition and cumulative sum techniques

Cited by 29 publications

References 32 publications

Automated quality inspection of surface defects on touch panels

Automated quality inspection of surface defects on touch panels

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

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

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