Detection of solar panel defects based on separable convolution and convolutional block attention module-Reference-Cited by-同舟云学术

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Detection of solar panel defects based on separable convolution and convolutional block attention module

Published:2023-06 Issue:3 Volume:45 Page:7136-7149
ISSN:1556-7036
Container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
language:en
Short-container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Author:

Yang Xiyun¹,Zhang Qiao¹,Wang Shuyan¹,Zhao Ya¹

Affiliation:

1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

Publisher

Informa UK Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567036.2023.2218301

Reference21 articles.

1. Identifying defective solar cells in electroluminescence images using deep feature representations

2. Hybrid mutation moth flame optimization with deep learning-based smart fabric defect detection

3. Automated defect identification in electroluminescence images of solar modules

4. Xception: Deep Learning with Depthwise Separable Convolutions

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1. Soldier Detection Based on Deep Learning and Attention Mechanism;2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS);2024-05-31

2. Crack Detection of Concrete Based on Improved CenterNet Model;Applied Sciences;2024-03-17

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