Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning
Binbin Ni, Pingguo Zou, Qiang Li, Yabin Chen
Available Online December 2018.
- https://doi.org/10.2991/tlicsc-18.2018.27How to use a DOI?
- Deep learning; Neural network; Photovoltaic modules; Defect detection.
- Currently, photovoltaic module manufacturers still rely on manual detection of EL images of photovoltaic modules to identify hidden defects. EL image detection is an important link in the quality control of photovoltaic modules production. Manual detection leads to slow detection speed, and the accuracy is affected by personal subjective judgment. In this paper, an intelligent defect detection method based on deep learning is proposed. The method first builds a network according to the sample characteristics. The initial network value is obtained through training. Then, the neural algorithm is used to adjust the network parameters to obtain the mapping relationship between training samples and defect-free templates. Finally, the comparison between reconstructed image and defect image is used to realize defect detection of test samples. Experiments show that the method based on deep learning can detect defects accurately and quickly.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Binbin Ni AU - Pingguo Zou AU - Qiang Li AU - Yabin Chen PY - 2018/12 DA - 2018/12 TI - Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning BT - 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/tlicsc-18.2018.27 DO - https://doi.org/10.2991/tlicsc-18.2018.27 ID - Ni2018/12 ER -