Abstract
In order to realize automatic inspection of printed circuit board (PCB) surface defects, the PCB defect automatic recognition technologies based on image processing and machine vision were studied. Firstly, the detected PCB color image and the standard PCB color image were grayed, and the visual effect of the detected image was improved by wavelet denoising and histogram equalization enhancement techniques. Secondly, the detected image and the standard image were calibrated, and the defects were extracted through the differential processing between them. Thirdly, the defect images were processed by the Otsu image segmentation and morphological method to get the binary images. The defect features were extracted and marked on the images. Finally, the type of the defects was determined according to the defect characteristics and its neighboring image. Five defects such as short circuit, open circuit, sag, bulge and hole, were detected and identified. Experimental results showed that the detectable rate of defects was 100%, and the recognition rate was over 90%, which can meet the need of real-time detection of industrial production lines.
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