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 …
Solar cell surface defect detection based on optimized YOLOv5 SHA LU1, KAIXING WU2, and JINXIN CHEN3 1,2,3School of Information and Electrical Engineering, Hebei University of Engineering, HanDan ...
Index Terms—automatic defects detection, solar cell, near-infrared image, attention network, region proposal network I. INTRODUCTION T HE multicrystalline solar cell defects lead to a seriously negative impact on the power generation efficiency. Thus, defect detection is very crucial to avoid the defective solar cell entering the next production stage …
Chen et al. 19 developed a novel solar CNN architecture to classify defects in visible light images of solar cells. Han et al. 20 proposed a deep learning-based defect …
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 …
Electroluminescence (EL) imaging is one of the main non-destructive inspection methods for quality assessment in the Photovoltaic (PV) module production industry. EL test reveals PV …
Due to various real-world conditions and processes, solar panels develop faults during their manufacturing and operations. The objective of this work is to build an End-to-End …
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 ...
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 …
In recent years, the solar cells defect detection method based on deep learning has the char-acteristics of high precision, fast speed, and strong robustness, and has achieved certain appli-cation effects. For example, Tang et al. [3] used Generative Adversarial Network (GAN) to generate many high-resolution datasets of cell defects, Convolutional …
An automatic method is proposed for solar cell defect detection and classification. An unsupervised algorithm is designed for adaptive defect detection. A …
The author in [4] presents an innovative solar cell defect detection system emphasizing portability and low computational power. The research utilizes K-means, MobileNetV2, and linear discriminant algorithms to cluster solar cell images and create customized detection models for each cluster. This method effectively differentiates between
Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection ...
Defect #2 – Scratches on the glass. A major and prevalent quality issue are scratches on the glass cover of the solar module. On average, small and large scratches on the thin glass covers are found during more than 70% of independent 3rd party quality inspections as for example performed by Sinovoltaics Consultancy Services.. These scratches are in many cases a result …
In this paper, data analysis methods for solar cell defect detection are categorised into two forms: 1) IBTs, which depend on analysing the deviations of optical …
Nowadays, renewable energies play an important role to cover the increasing power demand in accordance with environment protection. Solar energy, produced by large solar farms, is a fast growing technology offering environmental friendly power supply. However, its efficiency suffers from solar cell defects occurring during the operation life or caused by environmental …
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 emerged.
A defect-free solar cell surface may involve various texture patterns in different regions. This makes the inspection task extremely difficult. Image processing techniques have also been applied to the inspection of solar wafers and solar cells. Fu et al. [11] developed a machine vision algorithm to detect cracks in solar cells. The method can ...
The detection method mainly focuses on deploying a mathematically-based model to the existing EL systems setup, while enhancing the detection of micro cracks for a full-scale PV module containing 60 solar cells that would typically take around 1.62s and 2.52s for high and low resolution EL images, respectively. We have used a colure-coding structure for …
Traditional solar cell surface defect detection methods contain laser scanning method, ... The later improvements are all made for the case of target crowding and occlusion [32, 33], while there is no occlusion in the solar cell surface defects used in this paper, but there is a dense phenomenon of targets in part. In order to further improve the detection accuracy …
Cracking detection of silicon wafers of solar cells based on machine vision. Comb. Mach. Tool Autom. Process. Technol. (12), 95–97 (2019) Google Scholar Balzateguo, J., Eciolaza, L., Arexolaleiba, A.: Defect detection on polycrystalline solar cells using electroluminescence and fully convolutional nerural networks. In 2020IEEE/SICE ...
Different cell defects have different consequences, e.g. dark area leads to an immediate reduced power output while a crack can cause a reduced power output in the future. For this reason, many operators of solar parks wish an automated detection of defect cells and a further classification of defect cells into various defect categories in
This paper uses Mosaic and MixUp fusion data enhancement, K-meansCC clustering anchor box algorithm, and CIOU loss function to enhance the model performance and shows that the improved YOLO v5 algorithm can complete the solar cell defect detection task more accurately while meeting the real-time requirements. A solar cell defect detection method with an …
Abstract: 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 …
We construct a polycrystalline solar cell defect edge (PSCDE) dataset, which is the first high-quality solar cell segmentation dataset. We adopt the electroluminescence imaging technique collecting 700 challenging defect images with 512×512 resolution, such as multi-scale defects, occlusion defects, dense tiny defects, low contrast defects ...
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 ...
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. The model firstly integrates five data enhancement methods, namely Mosaic, Mixup, hsv transform, scale transform and …
There is an increasing interest towards the deep detection of defects in several industrial products (e.g. Sarpietro et al. [] developed a deep pipeline for classification of defect patterns applied in Silicon technology).This interest motivated us to propose a new dataset and its benchmark for the classification of defects in solar cells.
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 …
Understanding of defect physics in perovskite-halide semiconductors is essential to control the effects of structural and chemical defects on the performance of perovskite solar cells. Petrozza ...
In photovoltaic modules or in manufacturing, defective solar cells due to broken busbars, cross-connectors or faulty solder joints must be detected and repaired quickly and …
Solar cell defect detection aims to predict the class and location of multi-scale defects in a electroluminescence (EL) near-infrared image [2], [3], which is captured and processed by the following defect detection system. As is shown in Fig. 1, this intelligent system contains four components: supply subsystem, image acquisition subsystem, image process sub- system, …
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 …
Nowadays, silicon solar plants consist of hundreds of thousands of panels. The detection and characterization of solar cell defects, particularly on-site, is crucial to maintaining high productivity at the solar plant. Among the different techniques for the inspection of the solar cell defects, luminescence techniques provide very useful information about the spatial …
For solar cell defect detection, Chen et al. [] proposed a cell crack defect detection scheme based on structure perception designing the structure similarity measure (SSM) function, using the nonmaximum value suppression method to extract candidate crack defects, the proposed SSM function has stronger crack defect protrusion and suppression of …
This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges …
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