# Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
## 1 Introduction  
Considering the energy efficiency improvement and carbon emission reduction, the demand for photovoltaic (PV) products is increasing. Meanwhile, it requires the PV inspection equipment to have the capabilities in mass-scale detection and possessing to realize the quality assessment of PV modules. In the quality inspection of PV modules, apart from the basic visual quality assessment, increasing attention has been raised to electroluminescence (EL) defect detection during practical industry applications. The detection principles are listed as follows: When a positive voltage is applied to the PV module, non-equilibrium charge carriers in the semiconductor material redistribute and recombine, emitting photons. These emitted photons are captured by EL detection equipment and converted into visible light signals. The optical sensors, such as CCD cameras, then capture these light signals. Through signal processing, the light signals are converted into a voltage format, generating an image that represents the surface current density distribution of the PV module [1]. By analyzing these images, the internal current transport and charge distribution within the PV module can be better understood, enabling assessment of the overall quality and performance. However, the conventional manual inspection methods currently used in the PV inspection industry have become bottlenecks, restricting both the production efficiency and product quality of PV products due to their reliance on subjective judgment by inspection personnel. Manual inspection in PV product manufacturing leads to lower overall production efficiency and yield rates for component manufacturers, and the need for manyinspectors further increases the labor cost of production. Although some EL image acquisition systems are equipped with automatic defect detection algorithms, their detection accuracy is not high enough to meet industrial production requirements. Therefore, these systems remain auxiliary tools in the production process and cannot achieve full automation. Furthermore, traditional defect detection algorithms [2; 3; 4; 5] can achieve good detection results for a single type of defect, but they lack effectiveness in handling various defects with diverse morphologies and sizes on actual production lines, thereby facing limitations in terms of generalizability. EL defect detection in PV modules is a typical multi-scale object detection problem [6]. Compared with natural image object detection problems, because of the large difference in defect scale and the imbalance of defect samples, this has always been a difficulty in the industry. [PERSON] [7] used grayscale differences to extract texture attributes of defect regions for defect recognition, while [PERSON] [8] introduced a strategy for detecting defects in multicrystalline silicon solar cells based on local segmentation and binary thresholding. However, these early strategies require manual feature selection, limiting their generality and accuracy. As scholars' interest in this field has grown, new approaches have emerged, such as using Kalman filtering to track defect trajectories [9], wavelet image fusion with region sharpness enhancement [10], image matching and fusion techniques [11], and the adoption of Random Sample Consensus (RANSAC) algorithm to eliminate erroneous feature matching [12]. Nevertheless, these traditional methods are still limited by strict requirements for image stability and can only operate in specific environments, failing to fully adapt to the pace of modern efficient and intelligent manufacturing. In 2017, a one-stage deep learning model [13] was used for defect detection in PV cell EL images, improving the challenges of complex background textures and imbalanced defect samples. The model achieved an accuracy of 74.7% for detecting two types of defects. In 2018, [PERSON] et al. [14] first validated the feasibility of using a Convolutional Neural Network (CNN) for EL image defect detection.