With the rapid development of electronic information manufacturing industry,the quality requirements for circuit boards are increasingly high,and traditional manual inspection methods are difficult to meet the high-precision and high-efficiency inspection needs.Therefore,this article proposes a PCB board image defect detection method based on Convolutional Neural Network(CNN).Firstly,image preprocessing techniques such as image enhancement,smoothing,and sharpening are used to improve the quality of the image,facilitating feature extraction and noise removal.Secondly,CNN is used for automatic defect recognition of preprocessed images.The results indicate that this method performs excellently in terms of accuracy and stability in defect detection.This detection method can not only improve the quality inspection efficiency and quality control level of PCB boards,but also be extended to other image inspection tasks in industrial production,with broad prospects in industrial applications.
defect detectionPCB boardconvolutional neural networkelectronics manufacturing industry
万方数据
维普
