# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 5 Conclusions  
Over the past decade, the research community has rapidly increased their productivity within automated quality control systems with deep CNN models. In our literature review, we found that research has tended to focus on supervised CNN-based models and only a handful of studies on unsupervised CNNs were observed. Out of the 27 reviewed articles, 25 were supervised, accounting for 92.5% of the total data. However, the training of any CNN model requires a large amount of data to guarantee the expected high accuracy and energy yield values. As a critical aspect, locating and subsequently replacing or repairing faulty solar panels is important because panel failures can affect the efficiency of increasingly crucial PV power plants. These facilities are built at a significant financial cost,and in many countries around the world they have a large share of electricity production, which is expected to increase in the future.  
Generally, RGB images are used to identify the physical location of PV strings on field, while IR cameras are the ones used to find faults within the units. The performance-enhancing effect of UAV drones during fault localization must be highlighted again. After that, the faults can be divided into visible defects and those invisible to the naked eye. Invisible defects can be further investigated with PL or EL tests, or even by examining electrical parameters. Every step of the process can be controlled with deep learning, including CNN models. Furthermore, all reviewed studies reported performance metrics (accuracy, recall, etc.) above 90% and IoU and mAP values above 70%, according to the models used.  
The reviewed articles show that YOLO models are well suited for detection and classification tasks, but underperform in classification. At the same time, YOLO models provide real-time data processing and are well suited for PV power plant condition assessments. It is our view that the two main topics for future work on artificial intelligence algorithms and solar cell fault detection will remain the application of new versions of YOLO algorithms and the design of custom CNN models. Furthermore, the continuation of intensive research on these two types of models could lead the scientific community to better performance and lower power consumption methods for solar panel failure detection. As the designated crucial challenges are addressed, the need for automated optical examination is expected to grow in the future, both in academia and in the industry.  
Conceptualization, I.B. and D.M.-K.; methodology, D.M.-K.; resources, D.M.-K.; data curation, D.M.-K.; writing--original draft preparation, D.M.-K. and M.J.; writing--review and editing, I.B. and M.J.; visualization, D.M.-K.; supervision, I.B.; project administration, D.M.-K.; funding acquisition, I.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Supported by the University Research Scholarship Program of the Ministry for Culture and Innovation from the National Research, Development and Innovation Fund.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & artificial intelligence \\ CNN & convolutional neural network \\ CPU & Central Processing Unit \\ CT & computerized tomography \\ DDPM & Denoising Diffusion Probabilistic Model \\ EL & electroluminescence \\ GAN & Generative Adversarial Networks \\ GPU & Graphics Processing Unit \\ IoU & Intersection over Union \\ IR & infrared \\ LED & light-emitting diode \\ LMFF & Lightweight Multiscale Feature Fusion Network \\ mAP & Mean Average Precision \\ MRI & magnetic resonance imaging \\ \end{tabular}