| Summary: | Vision-based inspection of printed circuit board (PCB) soldering defects was studied for preparing feature data and classifying
the overall PCB soldering defects on a PCB prototype into different classes. The image data of overall PCB soldering defects
on a PCB prototype was developed using an image sensor camera. Image data augmentation was conducted to enhance the
dataset volume. Image pre-processing included image resizing, image colour conversion, and image denoising. Watershed-based image segmentation was performed in the image post-processing to segmented images; then, feature extraction was
conducted using curvelet transform to prepare image feature data. The feature data as the statistical data include kurtosis,
contrast, energy, homogeneity, and variance. These data were analysed, and the percentage difference of mean values of
statistical data between image classes was calculated. Kurtosis had the highest percentage difference among the statistical
data. In the comparison of the mean values, kurtosis obtained 4.97% difference for the class of good and medium condition;
17.02% difference for the good and bad condition; and 12.08% difference for the bad and medium condition. Through
this analysis, kurtosis is considered more reliable data for the machine-learning based classification in this project. The
extracted data can be applied in future studies to classify overall solder joint defects on a PCB prototype by artificial neural
network in machine learning classification.
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