Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ''crack'', ''solder'' and ''intra-cell'' defects on the edges of the solar module closest to the ground after fire.
We published an automatic computer vision pipeline of identifying solar cell defects. Tools can handle field images with a complex background (e.g., vegetation). Tools can be applied to other kinds of defects with transfer learning. We compared the performance of classification and object detection neural networks.
Main challenges of defect detection in PV systems. Although data availability improves the performance of defect diagnosis systems, big data or large training datasets can degrade computational efficiency, and therefore, the effectiveness of these systems. This limits the deployment of DL-based techniques in practical applications with big data.
Nevertheless, review papers proposed in the literature need to provide a comprehensive review or investigation of all the existing data analysis methods for PV system defect detection, including imaging-based and electrical testing techniques with greater granularity of each category's different types of techniques.
Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems.
An automated EL image pre-processing pipeline for solar cell defect detection . To identify the module region, the background in the image is removed. A histogram is first used by mapping the spectral colour of the pixel intensity values to the binned colour ranges. This yields a background of colour purple (Fig. 7 (b)).
To detect defects, the deviation between the test image and the reconstructed one derived from the ICA basis images is then evaluated by computing the reconstruction error. Limitations of the proposed method include a lack of ability to identify the shape and location of defects.
Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ''crack'', ''solder'' and ''intra-cell'' defects on the edges of the solar module closest to the ground after fire.
solar cells, automatic detection of solar cell defects and solar station efficiency has become an imperative. Various research applications to automatically detect solar cell defects have been conducted, but there have been few investigations on EL imaging. Furthermore, these earlier recent studies [6–17] that relied on EL imaging were
For a fully automated defect detection, we introduce a deep learning based classification pipeline operating on the EL images. This includes image preprocessing for distortion correction, segmentation and perspective correction as well as a deep convolutional neural network for solar defect classification with special emphasis on dealing with ...
The results show that the optimized model achieves an mAP of 96.1% on the publicly available dichotomous ELPV dataset, and can identify and locate a variety of common defects in the …
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 …
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 ...
The results show that the optimized model achieves an mAP of 96.1% on the publicly available dichotomous ELPV dataset, and can identify and locate a variety of common defects in the PVEL-AD dataset, while the mAP can reach 87.4%, an improvement of 10.38% compared with the original YOLOv5 model, which enables the model to perform more accurately ...
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 ...
This paper proposes an innovative approach that integrates neural networks with photoluminescence detection technology to address defects such as cracks, dirt, dark spots, and scratches in solar cells.
For a fully automated defect detection, we introduce a deep learning based classification pipeline operating on the EL images. This includes image preprocessing for distortion correction, …
Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a …
Tsai D-M et al (2015) Defect detection in multi-crystal solar cells using clustering with uniformity measures. Adv Eng Inform 29(3):419–430. Google Scholar Bartler A et al (2018) Automated detection of solar cell defects with deep learning. In: 2018 26th European signal processing conference (EUSIPCO). IEEE
Solar cell defect detection poses challenges due to complex image backgrounds, variable defect morphologies, and large-scale differences. Existing methods, including YOLOv5, encounter limitations in adaptive learning scales and perceptual field sizes, affecting accuracy. This study addresses these issues by proposing an improved YOLOv5 algorithm, incorporating …
Abstract： Solar cell surface defect detection is an indispensable process in the production of photovoltaic modules. Automatic defect detection methods based on machine vision are widely used due to their high accuracy, real-time and low cost advantages. This paper reviewed the …
However, its efficiency suffers from solar cell defects occurring during the operation life or caused by environmental incidents. These defects can be made visible using electroluminescence (EL) imaging. A manual classification of these EL images is very time and cost demanding and prone to subjective inter-examiner variations. For a fully automated defect detection, we introduce a …
Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on …
This paper proposes an innovative approach that integrates neural networks with photoluminescence detection technology to address defects such as cracks, dirt, dark spots, …
Many existing solar cell defect detection methods focus on the analysis of electroluminescence (EL) infrared images un-der 1000nm-1200nm wave length. Chiou et al.[16] developed a regional growth detection algorithm to extract cracks defect Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network Kun Liu liukun@hebut .cn 1 The School of …
To address challenges in detecting defects of varying scales in solar cells, an enhanced YOLOv5 algorithm is proposed. This algorithm integrates the Convolutional Block Attention Module (CBAM) to improve feature extraction, incorporates the Bi-directional Feature Pyramid Network (BiFPN) for refined feature fusion, and introduces the FasterNet ...
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 …
The surface defects on solar cell panels show significant intra-class and minimal inter-class differences, combined with a complex background. Therefore, achieving high-precision automatic detection of surface defects on solar cell panels becomes challenging. We utilize advanced techniques in deep learning and computer vision to address this ...
Traditionally, defect detection in EL images of PV cells has relied on labor-intensive manual inspection, which are not only time-consuming but also prone to human errors and subjectivity (Bartler et al., 2018).Due to the rise of advanced imaging techniques and considerable progress in machine vision and artificial intelligence, innovative solutions have …
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 …
Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique.
This paper is based on visionpr <, uses C # language to locate and detect the defects of solar cells, and transmits the coordinate value of the center point of the solar cells and the environmental information of the appearance defects to the industrial manipulator, so as to realize the automation of the welding process. 1 overall structure design of vision positioning …
Abstract： Solar cell surface defect detection is an indispensable process in the production of photovoltaic modules. Automatic defect detection methods based on machine vision are widely used due to their high accuracy, real-time and low cost advantages. This paper reviewed the research progress of machine vision-
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