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 this study, PV-YOLOv12n is introduced as an optimized variant of YOLOv12n, tailored for defect detection in electroluminescence (EL) images of PV panels.
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In this work, we detect and localize bright spots in the given EL image of a PV solar panel. As a baseline, we first applied object detection models directly on PV panel images to identify bright
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Advances in automation, prediction, and management have enabled sophisticated fault detection methods to enhance system reliability and availability. This paper emphasizes the pivotal
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To address the current limitations of low precision and high image data requirements in defect detection algorithms based on visible light imaging, this paper proposes a novel visible light
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In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel
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Future research directions are recommended for both industry and academia to advance PV fault detection methods. delamination faults have different visual symptoms. EL infrared
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In this paper, we propose a robust machine learning (ML) based approach to accurately detect bright spots by optimally splitting the EL images of PV solar panels and engineering novel discriminative
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In this study, we design an effective PV defect detection model leveraging the PSA attention mechanism and transformer architecture. Figure 1 illustrates the overall framework of the
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Based on the experiences of the aforementioned researchers and the summary of existing photovoltaic module defect detection methods, this paper proposes ST-YOLO, specifically
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