In order to avoid such accidents, it is a top priority to carry out relevant quality inspection before the solar panels leave the factory. For the defect detection of solar panels, the main traditional methods are divided into artificial physical method and machine vision method.
With the deepening of intelligent technology, deep learning detection algorithm can more accurately and easily identify whether the solar panel is defective and the specific defect category, which is broadly divided into two-stage detection algorithm and one-stage detection algorithm.
The results of comparative experiments on the solar panel defect detection data set show that after the improvement of the algorithm, the overall precision is increased by 1.5%, the recall rate is increased by 2.4%, and the mAP is up to 95.5%, which is 2.5% higher than that before the improvement.
To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell.
Abstract: A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences.
Therefore, it is essential to detect defects in photovoltaic cells promptly and accurately, as it holds significant importance for ensuring the long-term stable operation of the PV power generation system.
Solar Cell Surface Defect Detection Based on Optimized YOLOv5
Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model for more accurate and comprehensive identification of defects in solar cells.
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Multi-scale YOLOv5 for solar cell defect detection
CHEN Yafang,LIAO Fei,HUANY Xinyu,et al.Multi-scale YOLOv5 for solar cell defect detection[J].Optics and Precision Engineering,2023,31(12):1804-1815.
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An efficient CNN-based detector for photovoltaic module cells …
We propose a novel method for efficient detection of PV cell defects using EL images. We use CLAHE algorithm to improve EL image contrast. We propose GCAM for aiding in distinguishing defects with similar local details. The experimental results show the proposed method is superior to state-of-the-art methods.
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Research on multi-defects classification detection method for solar ...
7 Design of classification detection scheme for solar cell defects. In response to the diversity and complexity of solar cell defects, and based on above experiments analysis, a classification detection scheme for solar cell defects is designed and shown in Fig 12. Fig 12. The classification detection scheme for solar cell defects.
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Deep Convolutional Neural Networks for Detecting Solar Cell …
Deep convolutional neural network (DCNN)-based fault detection for solar cells is proposed. This method builds a deep network with three convolution layers, one pooling layer, one fully connected layer, and one output layer using solar cell photos as the input and a distinguishing defect category as the detection goal. The parameter number ...
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Solar panel defect detection design based on YOLO v5 algorithm
Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect …
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Multi-scale YOLOv5 for solar cell defect detection
Compared with other algorithms, the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells, satisfying the demand for real-time, high-precision defect detection under industrial conditions in photovoltaic power plants.
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Research of Solar Cell Surface Defect Detection ...
According to the surface quality problem of the solar cells, the machine vision detection system is designed, and the intelligent detection and classification of theSolar cell defect recognition model can be achieved. According to the surface quality problem of the solar cells, the machine vision detection system is designed. Concept design of the visual inspection system, …
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Accurate detection and intelligent classification of solar cells ...
In this paper, addressing the challenges of low accuracy in detecting small surface defects on solar cells and limited defect categories, a lightweight solar cell detection model named YOLOPL is proposed. The contributions of this study are as follows: The introduction of YOLOPL, an optimized and improved solar cell defect recognition model ...
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Solar Cell Surface Defect Detection Based on Improved YOLO v5
Abstract: A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and perceptual ...
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Research of Solar Cell Surface Defect Detection System Based …
According to the surface quality problem of the solar cells, the machine vision detection system is designed. Concept design of the visual inspection system, hardware configuration and software work process are described in detail. In the experimental process, solar cell images are collected in the motion state, the image characteristics of all kinds of damage are extracted, and the …
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AI-assisted Cell-Level Fault Detection and Localization in Solar PV ...
To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell. We propose a hybrid architecture that contains an ...
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(PDF) Deep Learning Methods for Solar Fault Detection and ...
Stoicescu, " Automated Detection of Solar Cell Defects with Deep Learning," in 2018 26th European Signal Processing Conference (EUSIPCO), 2018, pp. 2035–2039.
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Multi-scale YOLOv5 for solar cell defect detection
Compared with other algorithms, the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells, satisfying the demand for real-time, high …
Learn More
AI-assisted Cell-Level Fault Detection and Localization in Solar PV ...
To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell.
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Design of Solar Cell Defect Detection System | SpringerLink
In this paper, the solar cell positioning and defect detection system is developed based on the solar cell welding machine and visionpro of Cognex company.
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High-Precision Defect Detection in Solar Cells Using YOLOv10 …
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and ...
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Deep Convolutional Neural Networks for Detecting Solar Cell …
Deep convolutional neural network (DCNN)-based fault detection for solar cells is proposed. This method builds a deep network with three convolution layers, one pooling layer, one fully …
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Solar panel defect detection design based on YOLO v5 algorithm
Semantic Scholar extracted view of "Solar panel defect detection design based on YOLO v5 algorithm" by Jingyi Huang et al. Skip to search form Skip to main content Skip to account menu. Semantic Scholar''s Logo. Search 223,127,036 papers from all fields of science. Search. Sign In Create Free Account. DOI: 10.1016/j.heliyon.2023.e18826; Corpus ID: …
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Defect detection on solar cells using mathematical ...
Solar cells or photovoltaic systems have been extensively used to convert renewable solar energy to generate electricity, and the quality of solar cells is crucial in the electricity-generating process. Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the …
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An efficient CNN-based detector for photovoltaic module cells …
We propose a novel method for efficient detection of PV cell defects using EL images. We use CLAHE algorithm to improve EL image contrast. We propose GCAM for …
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Solar panel defect detection design based on YOLO v5 algorithm
Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect detection methods. Firstly, it is improved on the basis of coordinate attention to obtain a LCA attention mechanism with a larger target range, which can enhance the sensing range ...
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Solar Cell Surface Defect Detection Based on Optimized YOLOv5
Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model for more accurate …
Learn More
Accurate detection and intelligent classification of solar cells ...
In this paper, addressing the challenges of low accuracy in detecting small surface defects on solar cells and limited defect categories, a lightweight solar cell detection …
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Anomaly Detection and Automatic Labeling for Solar Cell Quality ...
Keywords: Anomaly detection; Electroluminescence; Solar cells; Neural Networks 1. Introduction Quality inspection applications in industry are becoming very important. It is a requirement to move towards a zero-defect manufacturing scenario, with unitary non-destructive inspection and traceability of produced parts. This is one of the applications where image analysis with deep …
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High-Precision Defect Detection in Solar Cells Using …
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our …
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AI-assisted Cell-Level Fault Detection and Localization in Solar PV ...
To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell …
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