A real-time PCB defect detector based on supervised and semi-supervised learning

Fan He, Sanli Tang, Siamak Mehrkanoon, Xiaolin Huang, Jie Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

This paper designs a deep model to detect PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features in various resolutions to predict defects in different scales. To train the deep model, a dataset including 6 common types of PCB defects is established, namely DeepPCB, which contains 1,500 image pairs with annotations. Besides, a semi-supervised learning manner is examined to effectively utilize the unlabelled images for training the PCB defect detector. Experiment results validate the effectiveness and efficiency of the proposed model by achieving 98.6% mAP @ 62 FPS on DeepPCB dataset. DeepPCB is now available at: https://github.com/tangsanli5201/DeepPCB.

Original languageEnglish
Title of host publicationESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PublisherESANN (i6doc.com)
Pages527-532
Number of pages6
ISBN (Electronic)9782875870742
Publication statusPublished - 2020
Event28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium
Duration: 2 Oct 20204 Oct 2020

Publication series

NameESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Country/TerritoryBelgium
CityVirtual, Online
Period2/10/204/10/20

Bibliographical note

Publisher Copyright:
© ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

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