Global Organization
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.
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.
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.
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.
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.
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)).
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 …
Automatic crack defect detection for multicrystalline solar cells is a challenging task, owing to inhomogeneously textured background, disturbance of crystal grains pseudo …
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 …
Wang et al. presented a data augmentation method and category weight assignment model for PV cell defect detection through using channel attention and ResNet152 …
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, …
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 …
As a result, the development of intelligent and efficient defect detection techniques for photovoltaic cells has emerged as a critical focus and ongoing challenge in …
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 …
The main focus of the research was to detect visible defects on solar cells. The main contribution of this work is using webcam camera to develop a robust and low-cost …
The appropriate hyperparameters, algorithm optimizers, and loss functions were employed to achieve optimal performance in the seven-class classification of solar cell …
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 ...
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 …
Experimental results showed that the multispectral deep CNN model can effectively detect surface defects of solar cells, has higher accuracy and stronger adaptability …
the multi-defect classification detection method for solar cells defect detection. 1 Introduction Solar cells are the core components of photovoltaic power generation system in …
Aim at the characteristics of the mismatch defects of solar cells, the K-means clustering algorithm is employed to re-cluster exclusive anchor boxes for mismatch defects, so …
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 …
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 …
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 …
Experimental results showed that the multispectral deep CNN model can effectively detect surface defects of solar cells, has higher accuracy and stronger adaptability to large-area defects, but has weak feature …
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 …
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 …