To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of photovoltaic p...
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To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of
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In near-infrared defect detection of photovoltaic panels, challenges such as significant variations in defect scales, diversity and ambiguity in defect morphology, and difficulty in identifying small defect
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Improved DMMA-YOLO network for small defect detection in infrared images of photovoltaic panels Xinying Wang a b, Hongxin Wang a, Nana Mu b, Shimin Li a, Mengyu Kong a
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This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward
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Timely automated detection is crucial for maintaining power generation efficiency and ensuring equipment safety. This paper presents a lightweight enhanced YOLOv11n model for
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A Defect detection model for PV panel electroluminescence images: We developed a defect detection model tailored to EL images of PV panels, addressing the poor detection
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Aimed at the complex defects and environmental challenges of PV panels, we propose a Dynamically Adaptive and High-Efficiency Small Object Detection Network for Infrared
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Semantic Scholar extracted view of "Improved DMMA-YOLO network for small defect detection in infrared images of photovoltaic panels" by Xinying Wang et al.
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The increasing reliance on photovoltaic systems (PVS) for sustainable energy production necessitates efficient monitoring methods to ensure optimal performance and timely fault detection.
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In this article, a hot spot defect detection algorithm according to infrared images of aerial PV is proposed for practical engineering problems such as defects with different morphology, unclear
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