Reflection-area-based feature descriptor for solder joint inspection

Chiu, Shao-Nung; Perng, Ming-Hwei

doi:10.1007/s00138-006-0051-1

Cited by 22 publications

(14 citation statements)

References 13 publications

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“…Recently, several approaches for the inspection of solder joints were proposed (Ong et al, 2008;Chiu and Perng, 2007;Ko and Cho, 2000;Kim and Cho, 1995;Driels and Lee, 1988). Some of these methods compute simple features in a manually tiled binary image, others use the pixel intensities directly as input fea-…”

Section: Methodsmentioning

confidence: 99%

“…These approaches can be divided into two groups. The first group deals with special camera and lighting setups to gain the best image representation of the relevant features (Ong et al, 2008;Kim and Cho, 1995;Chiu and Perng, 2007). In the second group, the camera and lighting setup is often predetermined and the inspection is done by sophisticated pattern recognition methods.…”

Section: Introductionmentioning

confidence: 99%

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Welding Inspection Using Novel Specularity Features and a One-Class SVM

Timm¹,

Klement²,

Barth

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

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Abstract:We present a framework for automatic inspection of welding seams based on specular reflections. Therefore, we introduce a novel feature set -called specularity features (SPECs) -describing statistical properties of specular reflections. For classification we use a one-class support-vector approach. The SPECs significantly outperform statistical geometric features and raw pixel intensities, since they capture more complex characteristics and depencies of shape and geometry. We obtain an error rate of 9%, which corresponds to the level of human performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Welding Inspection Using Novel Specularity Features and a One-Class SVM

Timm¹,

Klement²,

Barth

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

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show abstract

“…Despite of multilayer tieredcolor illumination ability's to highlight the defective features of solder joints efficiently, this system requires three images of each solder joint to be scanned and processed, this can increase the computational time significantly. Chiu and Perng in [168] proposed a single tiered-color illumination setting by choosing an optimal incident angle (ϕ) that can do the inspection with processing a single image only as shown in Figure 18. ϕ must be carefully selected in order to mimic the efficiency of three layers tiered-color lights.…”

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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“…Three different types of image were sequentially captured as three layers of lights were turned on one after another and obtained 2D and 3D features of the images. An attractive approach for an illumination system was developed by using only one layer of tiered light and two features [10]. Insufficient, acceptable and excess solder joints were classified by two straight lines on the 2D feature plane.…”

Section: Illumination Designmentioning

confidence: 99%

Design and development of automatic visual inspection system for PCB manufacturing

Mar

Yarlagadda

Fookes

2011

Robotics and Computer-Integrated Manufacturing

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a b s t r a c tInspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The ''front-end'' inspection system includes illumination normalisation, localisation and segmentation. The ''back-end'' inspection involves the classification of solder joints using the Log-Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log-Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.

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Reflection-area-based feature descriptor for solder joint inspection

Cited by 22 publications

References 13 publications

Welding Inspection Using Novel Specularity Features and a One-Class SVM

Welding Inspection Using Novel Specularity Features and a One-Class SVM

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

Design and development of automatic visual inspection system for PCB manufacturing

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