TY - GEN
T1 - Super Resolution Reconstruction of MiniLED Based on Generative Adversarial Network
AU - Huang, Yican
AU - Zhong, Xiaopin
AU - Liu, Weixiang
AU - Wu, Zong Ze
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the development of industrial detection technology, image super-resolution reconstruction is used as an effective data enhancement technique to provide the possibility of higher detection accuracy. This paper focuses on the image super-resolution in industrial detection scenarios, with the following two main contributions: 1) Constructing our own dataset in industrial detection specific scenarios. 2) Proposing EASRGAN, an image super-resolution reconstruction network, to effectively increase the accuracy of subsequent detection. Experimental results show that EASRGAN achieves excellent image super-resolution reconstruction, and the reconstructed image can increase the accuracy of the classification network by 2.83%.
AB - With the development of industrial detection technology, image super-resolution reconstruction is used as an effective data enhancement technique to provide the possibility of higher detection accuracy. This paper focuses on the image super-resolution in industrial detection scenarios, with the following two main contributions: 1) Constructing our own dataset in industrial detection specific scenarios. 2) Proposing EASRGAN, an image super-resolution reconstruction network, to effectively increase the accuracy of subsequent detection. Experimental results show that EASRGAN achieves excellent image super-resolution reconstruction, and the reconstructed image can increase the accuracy of the classification network by 2.83%.
KW - Generative adversarial network
KW - Industrial detection
KW - Super-resolution reconstruction
KW - miniLED
UR - https://www.scopus.com/pages/publications/85189318593
U2 - 10.1109/CAC59555.2023.10450440
DO - 10.1109/CAC59555.2023.10450440
M3 - 会议稿件
AN - SCOPUS:85189318593
T3 - Proceedings - 2023 China Automation Congress, CAC 2023
SP - 7098
EP - 7103
BT - Proceedings - 2023 China Automation Congress, CAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 China Automation Congress, CAC 2023
Y2 - 17 November 2023 through 19 November 2023
ER -