A Deep Learning-based Generic Solder Defect Detection System

Shi Qi Ye, Chen Sheng Xue, Cheng Yuan Jian, Yi Zhen Chen, Jia Jiun Gung, Chia Yu Lin

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Automated optical inspection (AOI) is essential in the electronic manufacturing production line. Strict screening rules lead to a high false alarm rate of AOI. Many industries use AI models to classify defects. The lack of flawed data and the uneven distribution of categories is a big challenge for model training. Furthermore, the AI model must be retrained when adding new production line data, and the time cost is high. In order to reduce the false alarm rate and improve the generalization of the AI model, we build a deep learning- based generic solder defect detection system (GSDD) to classify defects into seven types. In GSDD, the color gradation adjustment module solves the problem of color difference, and the data augmentation module solves the problem of variable data. In the experiment, we use the data set provided by the enterprise to evaluate the accuracy of the model to 96%, and the model can be applied to different machines. Thus, GSDD is a general model and can efficiently detect defects.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面99-100
頁數2
ISBN(電子)9781665470506
DOIs
出版狀態已出版 - 2022
事件2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan
持續時間: 6 7月 20228 7月 2022

出版系列

名字Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

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???event.eventtypes.event.conference???2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
國家/地區Taiwan
城市Taipei
期間6/07/228/07/22

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