## 1 Introduction
[10] proposed a target detection method with enhanced polarization information, which can improve the accuracy of common target recognition results in road scenes under haze weather conditions. [PERSON] et al. [11] proposed an object detection method based on local contrast of polarization weights, which can detect objects directly on infrared polarized mosaic images. [PERSON] et al. [12] studied a method based on polarization imaging optical systems and a dark channel prior optimization version to improve the quality of underwater images collected in turbid environments, reducing the impact of diffusion on image acquisition and improving the image quality for automatic detection and recognition of underwater objects. [PERSON] et al. [13] designed a device based on circularly polarized terahertz waves and polarization-sensitive detectors, using the spectral information contained in broadband terahertz pulses, which allows for the study of birefringence and optic-axis orientation of glass fibers in polymers with a single measurement. [PERSON] et al. [14] proposed an electroluminescence defect detection method for PV cells based on improved YOLOv5 in order to maintain and inspect the cells of PV modules more efficiently and quickly, and the improved YOLOv5 algorithm achieved an average accuracy of 76.3%, which is 2.5% higher than the standard YOLOv5 algorithm. With the rapid development of deep learning, convolutional neural networks (CNNs) [15] have received widespread attention in defect detection of photovoltaic modules due to their powerful feature extraction and data processing capabilities. In practical applications, this method is usually divided into two categories. One is a two-stage object detection algorithm based on candidate boxes represented by R-CNN networks, which has high detection accuracy but a slow running speed; another is a single-stage object detection algorithm represented by the YOLO model which has a fast running speed but slightly lower recognition accuracy than two-stage networks. [PERSON] et al. [16] proposed a YOLO-PVtarget detection algorithm by reducing the extraction of deep trunk information, increasing the extraction of low-level defect information and using a PAN network to fusion target features. The algorithm achieved 94.55% AP on the photovoltaic module EL image data set. Jamming speed exceeds 35 fps. [PERSON] et al. [17] introduced a bottleneck CSP module, added a tiny target detection prediction head in YOLOv5 to mitigate tiny defect missing, utilized Ghost convolution to improve the model inference speed and reduce the number of parameters and proposed a PV panel defect detection method based on the bottleneck CSP's Ghost convolution and the tiny target prediction head (GBH-YOLOv5) based on YOLOv5, which raises the mAP performance index to 27.8%. In summary, the YOLO series network has shown good performance in defect detection, but there are still many difficulties in detecting small defect targets in photovoltaic cells, mainly manifesting as follows: (1) Traditional electroluminescence detection obtains grayscale images of photovoltaic cells, where small defect targets account for a small proportion of the entire image and the edges are blurred, making them susceptible to background interference. (2) With the continuous training and downsampling of the YOLOv7 network, many small defect features gradually disappear. (3) The bounding box loss of object detection algorithms is mostly based on the IoU metric, but it is not a good metric for small targets. To address the aforementioned issues, this paper proposes a small target detection method for photovoltaic cell defect polarization imaging based on improved YOLOv7. This method can more accurately identify and detect small defects in photovoltaic cells. The main contributions of this article can be summarized as follows:  
1. Introduce polarization imaging in electroluminescence to obtain polarization images of small defects in photovoltaic cells and analyze the polarization degree image as input to the YOLOv7 network.