# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 1 Introduction
In addition, the materials within will oxidize and corrode with time, and the EVA adhesive can delaminate as well, acting as accelerative factors to deterioration. The environment can also cause defects with hot spots, shadows from vegetation or buildings and other surface contaminations, such as dust subsidence. In addition to adversely affecting the energy production of PV systems, hot spots can reduce the lifetime of PV modules [31; 32]. To maximize PV power production potential, it is crucial to ensure the panel's optimal performance and longevity. It is also worth noting that experience has shown that damage can occur during the transport and installation of solar panels [32]. This damage can be either through cracks and fractures that were not observed during manufacture, or an amplification of defects that were already present at the time of manufacture. Electroluminescence testing is one of the more visual ways of detecting these defects and damage. In addition, they can be detected by measuring electrical parameters, but EL testing is one of the best ways to determine the exact location and type of defects [31]. Human interpretation and analysis of the resulting image is feasible for small panels and low sample numbers, but mass analysis of large sample numbers and large complex images is a lengthy and tedious human operation. Hence, the categorization and analysis of solar electroluminescence images using artificial intelligence has become a popular research topic in the scientific community in recent years. Artificial intelligence (AI) is a technology that allows machines to demonstrate human-like reasoning and capabilities, such as autonomous decision-making. Through the assimilation of vast amounts of training data, AI learns to recognize speech, probing and trends, proactively solve problems, and predict future circumstances and events. AI is not a single technology, but a set of technologies that can be combined to perform different types of tasks. These tasks can be very specific, such as solving language challenges or providing tourist assistance or even image analysis [33; 34]. There are several types and levels of artificial intelligence. Three main groups can be interpreted in terms of AI capabilities: narrow, general and super-intelligent AI. In terms of levels, they can be divided into several interrelated categories: machine learning, neural networks, deep learning and generative AI. AI is at the forefront of robotics, computer vision and language processing and has been applied in many sectors such as banking, healthcare, commerce and manufacturing [33; 34]. In recent years, with the evolution of AI technologies, CNNs have risen up to be a contender for the most effective tools for crack detection. They also play a key role in the improvement of PV module inspections in terms of accuracy and efficiency [31]. Convolutional neural networks (CNNs) are deep learning algorithms that are specifically designed to process grid-based data. They have revolutionized image analysis tasks, largely due to advances in the computational power of electronic devices (CPUs and GPUs) and the availability of big data on the Internet [35]. They are excellent for object recognition and detection. Furthermore, CNNs are able to detect repeating patterns and structures in images, making them suitable for identifying crack patterns. Some material properties, such as brittleness or hardness, can affect the shape and propagation of cracks. CNNs have achieved significant success not only in defect detection in solar cells, but also in other material science applications, such as face and object recognition (e.g., security systems, facial recognition), medical image diagnostics (automatic analysis of CT and MRI images), and vehicle cameras (object identification, traffic signage) [31; 35; 36; 37].