A PCB surface mount component defect detection system based on AdaBoost and decision trees was designed to address the problems of low accuracy,low efficiency,and incomplete defect types in tradi-tional detection systems.The system detects chip pin and resistor defects.The system first performs image stitching,correction,component positioning,and noise reduction on the collected images.Then,it divides the surface mount components into regions and extracts shape,grayscale,and texture features from each sub-region.The AdaBoost algorithm is used to treat each feature as a weak classifier,select the optimal fea-tures to form a strong classifier iteratively,and output a feature code for each defect through a signal func-tion.Finally,the defect classification is achieved by querying the decision tree.Experimental results show that compared with traditional image processing defect detection systems,the system designed in this paper has significant advantages in detecting diverse defects,detecting speed,and accuracy.
维普
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