@inproceedings{42fc35c80a5f443d9c83c1396b1f4ff8,
title = "Image Blending Methods for Defective PCB Image Generation",
abstract = "Recent works have shown that deep learning models effectively assist in the task of image classification, which is often applied in industrial problems, e.g., defective PCB detection. However, training a model for defect detection requires lots of training data, but nowadays, it is challenging to obtain defective samples due to the production line is stable and mature. Therefore, we design two defective PCB image generation methods for different defect types. The proposed generation methods can produce more defective PCB images as much as we want, and experimental results show that our proposed methods can generate realistic defective images.",
author = "Chiang, {Ting Hui} and Chang, {Chun Hao} and Chen, {Li Hsin} and Lin, {Chun Ju} and Luo, {An Chun} and Deng, {Yu Shan} and Chang, {Po Han} and Dai, {Ming Ji} and Tseng, {Yu Chee}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",
year = "2022",
doi = "10.1109/ICCE-Taiwan55306.2022.9869213",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "261--262",
booktitle = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",
address = "美國",
}