Purpose – This study aimed to developed a defect detection system for a segment-type display module panel. Design/methodology/approach – The system included a data acquisition card, a video camera, a computer and a display module on a testing table. The video camera captured the display pattern of the display module and transferred it to the computer through the data acquisition card. The dynamic multi-thresholding method and analysis as well as back propagation neural network classification was used to classify the detected defects. Findings – The threshold values for the brightness at different positions in the display module image were obtained using the neural network and then stored in the look-up table, using two to six matrixes. Originality/value – The recognition speed was faster and the system was more flexible in comparison to the previous system. The proposed method, using unsophisticated and economical equipment, was also verified as providing highly accurate results with a low error rate.

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