Research on PCB Defect Detection Using Deep Convolutional Nerual Network

Guangzai Ran, Xu Lei, Dashuang Li, Zhanling Guo

20202020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)29 citationsDOI

Abstract

In view of the low robustness of the existing traditional PCB defect detection algorithms, this paper applies a PCB defect detection and recognition algorithm based on deep convolutional nerual network framework SSD(Single Shot Detector). This algorithm structure utilizes multi-scale feature maps to customise boundary boxes with different scales, and applies small convolution kernel (3*3)to predict the classification results and boundary box information. Then the detection results gracefully optimize by non-maximum suppression (NMS). Finally, in order to prove the superiority of this algorithm, this paper conducts comparative experiments. The experimental results show that the algorithm has a significant improvement in the accuracy of PCB defect detection, and the identification accuracy of PCB nodules can be as high as 94.69%. It has good applicability in the application of PCB defect detection.

Topics & Concepts

Robustness (evolution)Computer scienceKernel (algebra)Convolution (computer science)DetectorArtificial intelligenceAlgorithmBoundary (topology)Convolutional neural networkPattern recognition (psychology)Feature extractionDeep learningMathematicsArtificial neural networkTelecommunicationsGeneChemistryCombinatoricsBiochemistryMathematical analysisIndustrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsImage and Object Detection Techniques