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
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 established. Based on the YOLOv5 algorithm, the loss function is
Aiming at the problem of dificult operation and maintenance of PV power plants in complex backgrounds and combined with image processing technology, a method for detecting hot spot defects in infrared image PV panels that combines seg-mentation and detection, Deeplab-YOLO, is
In the specific case of solar panel detection, the availability of labelled and diversified datasets is essential to teach models to recognize patterns associated with defects and malfunctions. As a result, the main drawback of automated detection on higher-resolution images collected through UAV flights is the lack of sufficient datasets to train current state-of-the-art
The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical
Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV...
Real-time detection of PV modules in large-scale plants under varying lighting conditions. Automatic monitoring and evaluation of individual PV module performance. Development of monitoring and simulation methods using 3D remote sensing data.
In order to improve the speed and accuracy of photovoltaic panel occlusion detection, this paper proposes the target detection algorithm Seg-YOLO, introduces EIOU loss function, and combines CBAM attention mechanism. The performance of the improved algorithm in small target detection has been greatly improved. It provides a simple
solar cells, among which YOLOv5 algorithm worked best, with a leveling accuracy of 88.2%, which ensured the detection speed while maintaining good accuracy. The above research has greatly improved the speed and accuracy of solar photo-voltaicpaneldefectdetection,butduetothecomplexbackground ofphotovoltaicpanel
Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. 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 established. Based on the YOLOv5 algorithm, the loss
Aiming at the problem of dificult operation and maintenance of PV power plants in complex backgrounds and combined with image processing technology, a method for detecting hot
Real-time detection of PV modules in large-scale plants under varying lighting conditions. Automatic monitoring and evaluation of individual PV module performance.
Abstract: Occlusion will reduce the amount of solar radiation received by PV modules, such as leaves, bird droppings and other structures, which can affect the heat dissipation of PV modules and seriously reduce the power output. Aiming at the impact of occlusion, to identify and
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 established. Based on the YOLOv5 algorithm, the loss function is modified, the Segment Head detection module is introduced, and the convolutional block
Solar photovoltaic (PV) panels are pivotal in renewable energy generation, yet their efficacy can be severely hampered by hotspots induced by various factors. This study introduces a pioneering Expand. Highly Influenced. 2 Excerpts; Save. YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial
The experimental results show that the proposed method can detect the temperature of the photovoltaic panel in real time and can identify and locate the hot spot effect of the photovoltaic cell...
Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position
Individuals have been trying to develop a detection system for hot spots of PV panels. Chiou et al. [10] pointed out the hidden crack defects of batteries caused by the detection method of hot spots in PV panels based on the infrared image, established the near-infrared (NIR) imaging system to capture images of the internal cracks, and developed a kind of regional
In this paper is investigated the electrical performance and thermodynamics analysis under the shading shapes and shading ratios of photovoltaics panels which have in 75 W power. The operating...
The experimental results show that the proposed method can detect the temperature of the photovoltaic panel in real time and can identify and locate the hot spot effect of the photovoltaic cell...
In this paper, an improved YOLO-PX algorithm is proposed to identify and classify the occlusion of photovoltaic modules. Target detection experiments are carried out on the field data set of photovoltaic power station by using the original YOLO algorithm and the improved YOLO-PX algorithm.
In this paper, an improved YOLO-PX algorithm is proposed to identify and classify the occlusion of photovoltaic modules. Target detection experiments are carried out on
In order to improve the speed and accuracy of photovoltaic panel occlusion detection, this paper proposes the target detection algorithm Seg-YOLO, introduces EIOU loss
In this paper is investigated the electrical performance and thermodynamics analysis under the shading shapes and shading ratios of photovoltaics panels which have in
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
The real benefit of our approach is evident for single-panel occlusion faults where our model achieves a 98.78% accuracy as compared to 80.49% accuracy for the baseline method. Our approach also improves the precision from 0.7188 to 0.9677, recall from 0.7667 to 1.00, and F1 score from 0.7419 to 0.9836. Figures 11(c) and 12(c) show the confusion matrix of classifying
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
A lightweight solar panel fault diagnosis system based on image pre-processing and an improved VGG-19 network is proposed to address the problem of blurred solar panel field images, which are not easy for defects detection. ABSTRACT The share of renewable energy in the electricity market is increasing year by year. It is necessary to identify damage of solar
Abstract: Occlusion will reduce the amount of solar radiation received by PV modules, such as leaves, bird droppings and other structures, which can affect the heat dissipation of PV modules and seriously reduce the power output. Aiming at the impact of occlusion, to identify and classify occlusions on photovoltaic modules, an improved YOLOv5
Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV
This article proposes a Deeplab-YOLO hot-spot defect detec-tion method that combines segmentation and detection with infrared images and based on the diferences and features in the shape, size, and color of PV panels and hot spots. On the one hand, it can meet the accuracy of segmentation and enhance the edge features of the target.
Diferent anno-tation software is used to create a dataset with PV panels and hot spots as the target, respectively, segment the panels using an improved Deeplabv3+ model to exclude bright spots caused by endothermic objects in the background, and then use a one-stage object detection algorithm YOLO v5 to identify hot spots on the PV panels.
Aiming at the problem of dificult operation and maintenance of PV power plants in complex backgrounds and combined with image processing technology, a method for detecting hot spot defects in infrared image PV panels that combines seg-mentation and detection, Deeplab-YOLO, is proposed.
Guan et al. pro-posed a combined semantic segmentation and target detec-tion method for PV panel hot-spot detection. The PV panels are identified in the infrared images using improved YOLO v4, and the PV panels are extracted to segment the hot spots with improved Deeplabv3+.
The experimental results show that the optimized Deeplabv3+ model and YOLO v5 model improve the accuracy of segmenting PV panels in images and identifying hot-spot defects by 2.61% and 0.7%, respectively, compared with the original model.
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