Abstract: Solar panels are the core components of photovoltaic power generation systems, and their quality is directly related to safety and power generation efficiency. Therefore, surface defect
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In summary, the LW-PV DETR model can effectively process the infrared images of PV panels captured by UAVs and provides technical support
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Ensuring the quality of photovoltaic cells is paramount for enhancing the efficiency of solar energy systems. Traditional defect detection methods struggle with feature extraction and suffer from
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Abstract In the quality inspection of photovoltaic (PV) modules, defect detection methods that combine electroluminescence (EL) imaging with deep learning have attracted considerable
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A custom dataset was constructed by combining a public PV panel defect database with field-collected images, further expanded through data
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Table II presents the Average Precision (AP) comparison of various algorithms across five typical types of photovoltaic panel defects, further
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Developing efficient surface contaminants and defect detection algorithms for PV panels can facilitate automated and intelligent maintenance by robotic systems in large-scale PV power
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As global solar capacity approaches 10 TW by 2030 (2024 Renewable Energy Market Report), surface defect detection has become mission-critical. This article breaks down the latest international
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This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust,
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