@inproceedings{ed5fd4e5744c407bbb735bf23c5009c4,
title = "Automatic Industry PCB Board DIP Process Defect Detection with Deep Ensemble Method",
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].",
keywords = "AOI, deep learning, defect detection, Faster RCNN, machine learning, PCB, ResNetl01, solder, YOLO",
author = "Li, {Yu Ting} and Paul Kuo and Guo, {Jiun In}",
year = "2020",
month = jun,
doi = "10.1109/ISIE45063.2020.9152533",
language = "English",
series = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "453--459",
booktitle = "2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings",
address = "United States",
note = "29th IEEE International Symposium on Industrial Electronics, ISIE 2020 ; Conference date: 17-06-2020 Through 19-06-2020",
}