Photovoltaic Array Extraction Algorithm Based on Modified U2Net
Article No.: 47, Pages 1 - 6
Abstract
The production, transportation, and installation of photovoltaic (PV) modules can lead to component defects. These defects affect the power generation efficiency and can cause local temperature anomalies, leading to hot spots. Collecting infrared images of photovoltaic power stations using drones equipped with gimbals efficiently detect hot spots in PV modules. The extraction of PV arrays is crucial to eliminate the interference of complex scenes in infrared images. Since infrared thermal images only include temperature information without color information, extracting photovoltaic arrays from infrared thermal images is more challenging than visible-light images. Traditional methods rely on manually preset segmentation thresholds and are easily affected by the environment, resulting in low robustness. This paper proposes an improved image segmentation network, Attention-U2Net, based on the U2Net network structure combined with the CBAM attention mechanism. This network enhances the perception of local details in space and the feature extraction capability. Experimental results show that the method proposed in this paper can effectively extract photovoltaic arrays in complex scenes, demonstrating certain advantages over existing approaches.
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Index Terms
- Photovoltaic Array Extraction Algorithm Based on Modified U2Net
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Published In
April 2024
381 pages
ISBN:9798400716393
DOI:10.1145/3661725
Copyright © 2024 ACM.
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Published: 20 June 2024
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CMLDS 2024
CMLDS 2024: 2024 International Conference on Computing, Machine Learning and Data Science
April 12 - 14, 2024
Singapore, Singapore
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