# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 2 Imaging Methods and CNN Processing
### CNNs  
The application of CNNs is already widespread within vision-based industrial inspection systems. CNN inspection tasks can be divided into defect classification and defect localization [35; 38]. The analysis of EL images of solar cells and modules is an advanced image processing and machine learning task. Depending on the needs of the task, one of the following architectures has to be chosen [58; 59; 60; 61; 62]:  
Figure 2: This figure shows some common types of polycrystalline silicon solar cell defects: (**a**) crack, (**b**) fracture, (**c**) soldering failure, (**d**) hotspot, (**e**) finger interruption, (**f**) tabbing disconnection, (**g**) material defect, (**h**) edge defect (contamination during the silicon ingot growth), (**i**) corrosion, (**j**) PID phenomenon, (**k**) black core, and (**l**) backsheet scratch. Classification: A simple and quick to learn process. The goal is to classify the quality (e.g., \"defective\" vs. \"flawless\") of solar cells. Some solutions to the task are Custom CNN, VGG16, VGG19, ResNet50, ResNet101, MobileNetV2, and EfficientNet [63; 64; 65; 66; 67; 68]. * Fault localization (segmentation): a more complex process, which means the precise (pixel-wise) determination of the location of faults within a module. Examples include, but are not limited to, U-Net, SegNet, DeepLabV3/V3+, PSPNet, and HRNet [69]. Defect localization requires greater accuracy than identification. To construct a defect localization model, and thus to find the location of a defect, an encoder and a decoder have to be used together and low-level features have to be fused together in the decoding process to generate a high-resolution feature map. The design improvements of feature extractors and image fusion applications are hot research topics in defect location. One such widely used encoder-decoder architecture is U-Net [35]. Object Detection means the automatic detection and categorization of different types of defects, e.g., YOLOv5, YOLOv8. The YOLO (You Only Look Once) model is also used to analyze EL images of solar cells, which is a third direction, complementing the two main categories. It is not a classification because it does not just tell whether there is a defect or not. It is also not a segmentation because it is not pixel-accurate, but it is close to segmentation because it also performs positioning. It uses bounding boxes to determine the location of a fault, e.g., it draws around cracks. A big advantage is real-time processing, as it is very fast in an ideal industrial environment [70; 71; 72; 73; 74; 75; 76; 77]. Another special category is anomaly detection, where unknown defects are identified for the model, which are not part of the training data. It is significantly different from traditional classification or segmentation, e.g., Variational Autoencoder (VAE), AnoGAN, or One-Class SVM [78; 79]. In defect classification, CNNs have several components that are pivotal to their functions. One such component is the convolution layer, which contains and then applies the learning-capable filters (kernels) to the input data. Every single filter is applied to the input image to identify peculiar features. The first layers are tasked to find the edges, while the latter layers recognize more intricate patterns [31; 36; 37]. This is followed by pooling layers, which reduce the size of the data, thus reducing the complexion of the computation but still retaining most of the relevant information. Some prevailing pooling operations are max and average pooling. Nonlinear activation functions are placed on the layers' outputs, such as Rectified Linear Units (ReLUs), which bring nonlinearity into the network, allowing complex relationships to be learned. In the later stages of the network, there are usually fully connected layers that connect each neuron to all neurons in the next layer, ensuring high-level decision-making [31].