###### Abstract
The eccentric photorefraction method is a fast and effective screening tool that can be used to detect significant refractive errors. As Figure 1 shows, when the refractive error of the eye exceeds a critical value, a halo appears at the pupil edge. This characteristic is used for refractive error measurement. However, the lack of labeled datasets for real human eyes in eccentric photorefraction remains a major bottleneck. To address these challenges, this paper proposes an online vision screening solution based on the eccentric photorefraction method. The method uses a transfer learning approach, fine-tuning a pretrained model-eye model deployed on a server using a small amount of local data, thereby alleviating the issue of data scarcity. Data obtained from the screening equipment are transmitted via the cloud to the server, where the refractive error screening results are quickly generated by the deep learning-based algorithm. Compared to traditional refractive error screening methods, where trained nurses or technicians conduct visual acuity tests in schools, the method used in this study is more popular because it optimizes the refractive error screening process using online deployment and deep learning,  
Figure 1: (**a**) The raw infrared image acquired. (**b**) The segmented pupil region. (**c**) The change in the bright and dark areas of the pupil. making it more efficient and convenient. It can help economically underdeveloped areas conduct regular vision screening. The introduction of AI algorithms improves the extraction of features necessary for calculating refractive error, thus enhancing screening accuracy. The key contributions of this study are as follows:  
(1) This study innovatively proposes a deep learning network model based on the principles of eccentric photorefraction--CFGN (Comparative Feature-Guided Network). By combining multi-channel information fusion and a comparative feature-guided module, the model achieves optimal refractive error screening accuracy. (2) This study fully utilizes the optical characteristics of the objective eye model, which closely resemble those of real human eyes. An automated collection system is used to build a pretrained model for large-scale eccentric photorefraction infrared images of model eyes. Using the transfer learning method with fine-tuned pretrained models helps address the issue of data scarcity. (3) The refractive error screening technology proposed in this study can be easily integrated into an online refractive error screening system. This innovative approach is especially suitable for areas with limited medical resources and professionals, with broad application prospects and significant social value. The remainder of this paper is organized as follows. The Introduction and related work of our approach are presented in Section 1. Section 2 presents the online refractive error screening system and explains the research methods and content. Our dataset and experimental details are presented in Section 3, and some quantitative and qualitative experimental results were obtained to evaluate the effectiveness of our method. Subsequently, the effectiveness of this study as a whole and the shortcomings and research potential for improvement are discussed in Section 4. Finally, the Conclusions are stated in Section 5.