PCB Inspection Using Image Processing and Wavelet Transform

Santoyo, J.; Pedraza, J. C.; Mejía, L. Felipe; Santoyo, Alejandro

doi:10.1007/978-3-540-76631-5_60

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Once defined the pre-processing algorithm and image classification algorithm, were defined optimized implementation strategies of PCB image binarization time with time percentage reduction dependents of the groupment of three preprocessing steps. After a definition of an optimization metric through the new based algorithm for image binarization, was verified the percentage time reduction in the PCB image binarization, around 14.1% in comparison of the better result obtained in the work [8]. With this result, was possible obtain also an image classification efficacy in function of the optimized PCB binarized image.…”

Section: Resultsmentioning

confidence: 64%

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A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

Bonello¹,

Iano²,

Neto³

2018

IJMIP

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Various concentrated work has been developed in the area of computer vision applied to detection of failures on printed circuit boards (PCB's), aiming at reducing the possibility of the occurrence of the fabrication defects. In this research, based on PCI'swithout mounting reference and test layout models, the objective is to study is the application of an image subtraction technique to the failure detection of those bare printed circuit boards layouts using image subtraction techniques during the image processing. By developing the primary subtraction algorithm, one may compare the efficacy of this image processing technique using linear simulations developed in MATLAB.

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Section: Resultsmentioning

confidence: 64%

“…Image processing time. According to[8], the inspection time for PCB image binarization it's about 0.078s utilizing a classical approach in Matlab. In the new based algorithm, the binarization of the reference bare PCB image was about 0.067s as we can see in theFigure 7in the subtopic 2.6.1.…”

mentioning

confidence: 99%

A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

Bonello¹,

Iano²,

Neto³

2018

IJMIP

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“…Several other methods are based on different computer vision theories e.g. image segmentation or edge detection, Hough transform, watershed segmentation and wavelet-based approaches [25,2,18,27]. However, in most of the cases, these methods have lower detection accuracy and ultimately suffer from the problem of identification of fuzzy edges, corners, noise and precise Thermal and infrared imaging of PCBs came up as an alternative solution to test and inspect electronic components.…”

Section: Introductionmentioning

confidence: 99%

Thermal vulnerability detection in integrated electronic and photonic circuits using IR thermography

Hussain,

Jalil,

Pascali

et al. 2020

Preprint

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Failure prediction of any electrical/optical component is crucial for estimating its operating life. Using high temperature operating life (HTOL) tests, it is possible to model the failure mechanisms for integrated circuits. Conventional HTOL standards are not suitable for operating life prediction of photonic components owing to their functional dependence on thermo-optic effect. This work presents an IR-assisted thermal vulnerability detection technique suitable for photonic as well as electronic components. By accurately mapping the thermal profile of an integrated circuit under a stress condition, it is possible to precisely locate the heat center for predicting the long-term operational failures within the device under test. For the first time, the reliability testing is extended to a fully functional microwave photonic system using conventional IR thermography. By applying image fusion using affine transformation on multimodal acquisition, it was demonstrated that by comparing the IR profile and GDSII layout, it is possible to accurately locate the heat centers along with spatial information on the type of component. Multiple IR profiles of optical as well as electrical components/circuits were acquired and mapped onto the layout files. In order to ascertain the degree of effectiveness of the proposed technique, IR profiles of CMOS RF and digital circuits were also analyzed. The presented technique offers a reliable automated identification of heat spots within a circuit/system.

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“…Earlier works on PCB defect detection focus on waveletbased algorithms [4,5,6,7,8], which decreases the computation time compared to those based on image difference operation. Recently, [2] develops a hybrid algorithm to detect PCB defects by using morphological segmentation and simple image processing technique.…”

Section: Introductionmentioning

confidence: 99%

Online PCB Defect Detector On A New PCB Defect Dataset

Tang¹,

He²,

Huang³

et al. 2019

Preprint

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Previous works for PCB defect detection based on image difference and image processing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patterns or over-sensitivity about some hyper-parameters. In this work, we design a deep model that accurately detects PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features of a large range of resolutions, which are merged by group to predict PCB defect of corresponding scales. To train the deep model, a dataset is established, namely DeepPCB, which contains 1,500 image pairs with annotations including positions of 6 common types of PCB defects. Experiment results validate the effectiveness and efficiency of the proposed model by achieving 98.6% mAP @ 62 FPS on DeepPCB dataset. This dataset is now available at: https://github.com/tangsanli5201/DeepPCB.

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PCB Inspection Using Image Processing and Wavelet Transform

Cited by 4 publications

References 4 publications

A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

Thermal vulnerability detection in integrated electronic and photonic circuits using IR thermography

Online PCB Defect Detector On A New PCB Defect Dataset

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