## 1 Introduction  
Photovoltaic cells have high requirements for both conversion efficiency and working life, with even small defects having significant impacts. In addition, defects in these cells can generate excessive heat during the power generation process, potentially leading to cell module burnout due to overheating. This can result in fires within photovoltaic power plants, causing considerable economic losses [1]. An automatic defect detection method based on computer vision can identify and locate defects well, so it has a wide range of applications in the process of photovoltaic cell production and power plant operation and maintenance. Nevertheless, in practical applications, the accuracy of automatic detection is often limited by the small scale and subtle characteristics of defects. Therefore, how to achieve real-time and accurate detection of small defect targets is a practical problem that needs to be solved in the current operation and maintenance process of photovoltaic power stations, which has important practical significance for improving the performance of photovoltaic modules, extending the life of photovoltaic cells and reducing power generation costs [2]. Conventional photovoltaic cell defect detection techniques mainly include infrared thermal imaging, photoluminescence imaging and electroluminescence imaging. Among these, infrared thermal imaging technology [3] is a method of applying reverse bias to photovoltaic modules to photograph infrared rays emitted by the surface of solar cells after local heating. However, this method suffers from low detection efficiency, ignores the distribution property of series resistance and incurs high costs. Photoluminescence imaging technology [4] is a non-contact and non-destructive imaging method that excites fluorescence from the silicon layer of photovoltaic modules under specific light sources, without the need for electricity. However, this method has a high economic cost [5]. Electroluminescence imaging [6] technology applies reverse voltage to both ends of the photovoltaic module, so that the silicon material of the battery will glow due to the current passing through. This method has high imaging quality and moderate cost and is currently the mainstream solar cell image acquisition method in the photovoltaic field. Nevertheless, the acquisition of electroluminescent images is susceptible to environmental stray light and camera imaging characteristics, leading to background interference, random noise, image blur and overall quality degradation [7]. Additionally, the subtle difference in luminous intensity between the defect edge and the normal part poses challenges in achieving satisfactory detection results. Polarization is one of the essential characteristics of light radiation electromagnetic waves, less affected by the environment, so the use of polarization characteristics of imaging to obtain target information can improve the contrast between the target and the background and enhance the clarity of the target in the image. At present, polarization imaging target detection technology has been applied in many fields. [PERSON] et al. [8] demonstrated through experiments that polarization imaging can emphasize the contour edges and texture details of photovoltaic cell defect images. Additionally, the Laplace pyramid is employed to decompose the captured light-intensity images and polarization degree images, fusing high- and low-frequency information. A defect detection method for crystalline silicon photovoltaic cells based on electroluminescence polarization image fusion is proposed, effectively highlighting the defect characteristics of photovoltaic cells. [PERSON] et al. [9] proposed an improved YOLOv5 method for detecting small targets in polarization imaging by inputting polarization degree images at the input end, reducing C3 modules and incorporating coordinate attention mechanisms. This method enhances the feature extraction ability and detection accuracy of small target images. [PERSON] et al.