Automatic Industry PCB Board DIP Process Defect Detection with Deep Ensemble Method

Yu Ting Li, Paul Kuo, Jiun In Guo

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

Abstract

The conventional PCB (Printed Circuit Board) DIP (Dual Inline Package) process solder defect detection was done by labor inspection, which is not only time-intensive but also labor-intensive. This paper proposes a deep ensemble method to inspect the PCB solder defects to replace the labor inspection. To achieve a high detection rate and a low false alarm rate, two distinct detection models, a hybrid YOLOv2 (YOLOv2 as a foreground detector and ResNet-101 as a classifier) and Faster RCNN with ResNet-101 and FPN are separately trained to obtain a high detection rate result. The final ensemble model aggregates the result from the two detection models. That achieves a 96.73% detection rate and a 19.73% false alarm rate in real tests. The detection time is less than 15 seconds for inferencing a PCB image with a resolution of 7296∗6000. The proposed method has been proven efficient in terms of guiding operators to identify and fix PCB solder defects [1] and thus is able to reduce 33% of labor demand for each PCB production line at our real test site. [1].

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages453-459
Number of pages7
ISBN (Electronic)9781728156354
DOIs
StatePublished - Jun 2020
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 17 Jun 202019 Jun 2020

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2020-June

Conference

Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020
CountryNetherlands
CityDelft
Period17/06/2019/06/20

Keywords

  • AOI
  • deep learning
  • defect detection
  • Faster RCNN
  • machine learning
  • PCB
  • ResNetl01
  • solder
  • YOLO

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