For defect inspection using computer vision, deep learning models have been introduced to improve the conventional rule-based pattern analysis. A lot of data is a prerequisite to the success of them, but the on-the-spot industrial field suffers from lack of data. In this paper, we propose a deep metric neural network to improve the performance even with insufficient data imbalanced in class. The model is verified with the dataset of new products by evaluating the accuracy with 10-fold cross-validation. Our model is based on the data in the smallest category, 1.2 K, which achieves the highest performance of 90.42% using sampled pairs without using all the data for training. High accuracy has been achieved and proven applicability in the industry compared to the conventional machine learning models.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings|
|Editors||Cesar Analide, Paulo Novais, David Camacho, Hujun Yin|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|Publication status||Published - 2020|
|Event||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal|
Duration: 2020 Nov 4 → 2020 Nov 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020|
|Period||20/11/4 → 20/11/6|
Bibliographical noteFunding Information:
Acknowledgments. This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Samsung Electronics Co., Ltd.
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)