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PCB Defects Detection using Connected Component Classification  

Jung, Min-Chul (Dept. of Computer System Engineering)
Publication Information
Journal of the Semiconductor & Display Technology / v.10, no.1, 2011 , pp. 113-118 More about this Journal
Abstract
This paper proposes computer visual inspection algorithms for PCB defects which are found in a manufacturing process. The proposed method can detect open circuit and short circuit on bare PCB without using any reference images. It performs adaptive threshold processing for the ROI (Region of Interest) of a target image, median filtering to remove noises, and then analyzes connected components of the binary image. In this paper, the connected components of circuit pattern are defined as 6 types. The proposed method classifies the connected components of the target image into 6 types, and determines an unclassified component as a defect of the circuit. The analysis of the original target image detects open circuits, while the analysis of the complement image finds short circuits. The machine vision inspection system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.
Keywords
PCB defects; AOI; Vision Inspection; Adaptive Threshold; Median Filtering; Connected Component;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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