Nowadays, methods of photovoltaic panel defect detection are roughly divided into 2 types: one is manual inspection, and the other is machine vision and computer vision inspection.
When solar photovoltaic panel surface defect detection is applied to industrial inspection, the primary focus lies in achieving a highly accurate and precise model with exceptional localization capabilities, and the training model will basically not affect the detection speed.
Given the characteristics of photovoltaic power plants, deep learning-based defect detection models can be deployed on surveillance systems or drone patrols, enabling automated defect detection and ensuring the efficient operation and maintenance of photovoltaic panels.
Buerhop et al. 17 constructed a publicly available dataset using EL images for optical inspection of photovoltaic panels. Based on this dataset, researchers have developed numerous algorithms 9, 10, 12 for photovoltaic panel defect detection.
As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional
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In order to tackle this issue, this study presents a PV panel defect detection approach based on the advanced YOLOv11 object detection algorithm. The mosaic augmentation approach is first employed
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To objectively assess the effectiveness of our proposed method for photovoltaic panel defect detection, we conducted both quantitative and qualitative comparisons against established...
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The invention relates to the field of photovoltaic panel foreign matter identification, in particular to a photovoltaic panel foreign matter identification method suitable for...
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In order to detect photovoltaic panels in some special environments, a part of the dataset is selected for image processing, and the photovoltaic panel scene in some special scenarios is simulated by
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In this paper, the latest progress in the field of PV module fault diagnosis in recent years is reviewed, with emphasis on fault detection methods based on electrical characteristic parameters
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This identification algorithm provides automated inspection and monitoring capabilities for photovoltaic panels under visible light conditions.
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This paper focuses on the identification and detection of common occlusion problems on solar panel surfaces, including dust source identification, dust accumulation degree recognition, and
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In order to accurately obtain the occlusion area and position information of the PV panel, a PV module occlusion detection model based on the Segment-You Only Look Once (Seg-YOLO) algorithm is
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Nowadays, methods of photovoltaic panel defect detection are roughly divided into 2 types: one is manual inspection, and the other is machine vision and computer vision inspection.
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