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...
Recently, organic–inorganic metal halide perovskite materials have shown great potential for the next generation low-cost renewable energy generation source, and it has surpassed the power conversion efficiency (laboratory scale) of most of the commercially available solar absorbers. Despite the ubiquity and robustness of …
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 …
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 …
The experiments and simulation tests prove that the presented defect detection approach is superior to the conventional methods, and the proposed method is more stable and efficient. Electroluminescent (EL) plays an important role in the application of photovoltaic cell Defect detection. Traditional approaches for EL result analysis usually utilize visual …
An optimized YOLOv5 model is proposed for more accurate and comprehensive identification of defects in solar cells, which enables the model to perform more accurately while ensuring the real-time requirement of solar cell surface defects detection task. Traditional vision methods for solar cell defect detection have problems such as low …
As shown in Fig. 1, defects in PCB manufacturing cannot be completely avoided due to the varied manufacturing environment and available production resources.Any anomaly, including open, short, and burr, will damage the device''s functionality to varying degrees.
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 properties, thermal patterns, or other visual features in images, and 2) ETTs, which depend on comparing ...
International Journal for Multidisciplinary Research (IJFMR) E-ISSN: 2582-2160 Website: Email: editor@ijfmr IJFMR240319496 Volume 6, Issue 3, May-June 2024 1 Solar Cell Surface Defect Detection Based on Optimized Yolov5
To further optimize and improve the YOLO v5 algorithm, this paper uses Mosaic and MixUp fusion data enhancement, K-means++ clustering anchor box algorithm, and CIOU loss function to enhance the model performance. A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell …
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 …
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 …
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 …
M. Zhang, L. Yin: Solar Cell Surface Defect Detection Based on Improved YOLO v5 computer vision based on manual feature extraction and classi˝ers [7] [10]. Tsai et al. proposed a method for ...
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 …
According to the characteristics of the defects on the surfaces of solar cells, we proposed a solar cell surface defect detection method based on the fusion model of Faster R-CNN and R-FCN. The accuracy of the detection was improved by the complementary fusion of the detection results of the two models.
Nowadays, the photovoltaic industry has developed significantly. Solar photovoltaic panel defect detection is an important part of solar photovoltaic panel quality inspection. Aiming at the problems of chaotic distribution of defect targets on photovoltaic panels,... 3.1 SENet Attention Mechanism ...
The world''s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource …
Solar cell micro crack detection technique is proposed. • Conventional Electroluminescence (EL) is used to inspect the solar cell cracks. • The techniques is based on a Binary and Discreet Fourier Transform (DFT) image processing models. • …
The surface defects such as cracks, broken cells and unsoldered areas on the solar cell caused by manufacturing process defects or artificial operation seriously affect the efficiency of solar cell. For the surface defects of solar cell, which have the characteristics of various shapes, large-scale changes, and difficult to detect, a surface …
The experimental results show that the improved YOLO v5 algorithm achieves 89.64% mAP for the model trained on the solar cell EL image dataset, which is 7.85% higher than 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 …
SOLAR CELL DEFECT DETECTION AND ANALYSIS SYSTEM USING DEEP LEARNING 1Shrayanth S, 2Siddhartha J, 3Laxmi V 1,2Student, 3Associate Professor Information Science and Engineering BNM Institute of Technology/ VTU Karnataka, India. At ...
2. Defects in PbS-QDs Solar Cells 2.1. The Effects of Defects The influence of defects on the solar cell''s performance can be briefed in the low photo-electric current, short carrier lifetime, and deficit in the open circuit voltage. Traps induce inequity of charge [8]
Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the …
Aiming to address the difficulty of defect detection caused by complex solar cell backgrounds and diverse defects in the mixed scenario of monocrystalline cells and …
2.2. Input and preprocessing Solar cell EL images input: The series of captured EL images are taken as the input of the proposed method. Each image is denoted by C k (1 ≤ k ≤ K), where K is the total number of captured images. For each C k, the corresponding image information quadruple {j k, t k, f k, a k} is also obtained as input for …
In this research we present a Lock-In amplifier photoluminescence image (PLI) detection system. Using a commercially available CCD camera, combined with a modulated excitation light source system, Lock-In amplifier technique is employed to enhance the sensitivity and signal-to-noise ratio of the image. Using a high resolution …
The surface of solar cell products is critically sensitive to existing defects, leading to the loss of efficiency. Finding any defects in the solar cell is a significantly important task in the quality control process. Automated visual inspection systems are widely used for defect detection and reject faulty products. Numerous methods are proposed to …
Meng et al. [] proposed a solar cell defect detection method based on the improved YOLOv5 algorithm in view of the complex background of solar cell images, variable defect shapes, and large differences.
5 · Photovoltaic (PV) industries are susceptible to manufacturing defects within their solar cells. To accurately evaluate the efficacy of solar PV modules, the identification of manufacturing defects is imperative. Conventional industrial defect inspections predominantly rely on highly skilled inspectors conducting manual defect assessments, …
The surface defects of solar cells exhibit significant intra-class differences, minor inter-class differences, and complex background features, making high-precision identification of surface defects a challenging task. This paper proposes a Convolutional -Vision Transformer Network (CViT-Net) that combines local and global features to address this …