Performance evaluation of wavelet-based PCB defect detection and localization algorithm

Ibrahim, Zuwairie; Al-Attas, Syed Abdul Rahman; Aspar, Zulfakar; Mokji, Musa Mohd

doi:10.1109/icit.2002.1189895

Cited by 21 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ibrahim et al . [5] proposed an efficient algorithm for automated visual inspection of PCB inspection systems that can automatically detect and locate any defects on the PCB. Using wavelet‐based image difference algorithms to detect defects, excellent time performance demonstrates that second‐order Haar wavelet transforms is suitable for the application of automated vision in PCB inspection.…”

Section: Related Workmentioning

confidence: 99%

CNN‐based reference comparison method for classifying bare PCB defects

Peng

Liu

et al. 2018

J. eng.

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

CNN‐based reference comparison method for classifying bare PCB defects

Peng

Liu

et al. 2018

J. eng.

View full text Add to dashboard Cite

“…How it is employed to obtain the features proposed in this work will be described further on. The DWT has already been employed for the feature extraction step in the inspection of PCBs [17][18][19]. Each step to achieve these new features is described below.…”

Section: B Feature Extractionmentioning

confidence: 99%

A Multiple Neural Network System to Classify Solder Joints on Integrated Circuits

Acciani¹,

Brunetti²,

Fornarelli³

2006

IJCIR

View full text Add to dashboard Cite

The following paper introduces a diagnostic process to detect solder joint defects on Printed Circuit Boards assembled in Surface Mounting Technology. The diagnosis is accomplished by a Neural Network System which processes the images of the solder joints of the integrated circuits mounted on the board. The board images are acquired and then preprocessed to extract the regions of interest for the diagnosis which are the solder joints of the integrated circuits. Five different levels of solder quality in respect to the amount of solder paste have been defined. Two feature vectors have been extracted from each region of interest, the "geometric" feature vector and the "wavelet" feature vector. Both vectors feed the neural network system constituted by two Multi Layer Perceptron neural networks and a Linear Vector Quantization network for the classification. The experimental results are devoted to comparing the performances of a Multi Layer Perceptron network, of a Linear Vector Quantization network, and of the overall neural network system, considering both geometric and wavelet features. The results prove that the overall classifier is the best compromise in terms of recognition rate and time required for the diagnosis in respect to the single classifiers.

show abstract

“…Its typical example is plain and twill fabric. Apart from those that can be defined by wallpaper groups, there are other textures that consist of random, non-directional components or a mixture of patterned and non-patterned materials namely printed circuit boards (PCBs) [21,[23][24][25], wood [26][27][28][29][30][31][32], wafer [33], pearl [34], and rail [35].…”

Section: Introductionmentioning

confidence: 99%