@inproceedings{bba198420f6c4ee08056bd687025bfd3,
title = "EU-Net: A Novel Semantic Segmentation Architecture for Surface Defect Detection of Mobile Phone Screens",
abstract = "In this paper, a new semantic segmentation algorithm, EU-Net (Efficient U-Net), is proposed to realize surface defect detection of mobile phone screens. Compared with U-Net, the encoder and decoder of EU-Net are modified with EfficientNet-B0 and MBconv Block to enhance the detection efficiency and accuracy. Due to the loss of feature information in the cropping operation, it is removed in our EU-Net to improve the detection accuracy. In addition, conventional image processing techniques are used to enhance the dataset. The experiments are conducted on a dataset collected from a production site of the mobile phone screens to verify the superiority of the proposed algorithm.",
keywords = "Computer Vision, Deep Learning, EU-Net, Semantic Segmentation, Surface Defect Detection",
author = "Jiawei Pan and Deyu Zeng and Qi Tan and Zongze Wu and Zhigang Ren",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9727763",
language = "英语",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6589--6594",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
}