@inproceedings{6c262227dc8e417480afcb23e10a9872,
title = "Investigation of Generated Near-infrared Crack Defect Dataset for Solar Cells",
abstract = "The generation of near infrared electroluminescence (EL) crack defect images of polycrystalline silicon (poly-Si) solar cells is investigated. For complex environments (such as background object interference) and weak target signals (such as low pixel ratio), object detection is generally difficult, as is the case for the direct generation of such images. A typical example is the crack defect detection on poly-Si solar cells. The irregular complex environment may cover the defects and cause the generation model to learn irrelevant background and noise information, thus affecting the generation of defect images. Based on StyleGAN model and image processing methods, a two-stage “generation + fusion” simulation method is proposed to generate the EL crack defect images for poly-Si solar cells. The similarity between simulated and real images and the effectiveness of the simulated dataset for small sample learning are studied. The method can also be used for other similar industrial defect detection and data enhancement research.",
keywords = "Crack defect, Electroluminescence Imaging, Fr{\'e}chet Inception Distance, Generative Adversarial Network, Object detection, Solar Cells",
author = "Zuo Chen and Wende Liu and Taotao Zhang and Yadong Chen and Haiyong Gan and Limin Xiong",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 Conference on Spectral Technology and Applications, CSTA 2024 ; Conference date: 09-05-2024 Through 11-05-2024",
year = "2024",
doi = "10.1117/12.3037042",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zhe Wang and Hongbin Ding",
booktitle = "Conference on Spectral Technology and Applications, CSTA 2024",
address = "美国",
}