Deep-Learning-Based Research on Refractive Detection

Refractive error is a very common and highly detrimental ophthalmic problem to the development of visual function. Accurate and convenient refractive detection techniques are of great importance for timely detection of refractive error problems and for adopting corresponding measures for intervention. Although currently refractive screening device can quickly detect refraction, there are two main problems: the detection accuracy is low, and the requirements for the degree of cooperation of the tested people are high. Therefore, this paper proposes a new refractive detection method, which obtains face NIR images using an optical system based on the principle of eccentric photographic refraction, processes face NIR images using image processing technology to obtain left and right pupil images and pupil position information. Then, using the mixed data multi-input neural network model proposed in this paper that combines the depth separable convolution and SE module for training and diopter calculation. Compared with refractive detection methods, which are based on the principle of eccentric photographic refraction, this method is expected to achieve higher accuracy with the expansion of the data set, and this method, which uses pupillary position information as input to the model, can solve the problems of traditional algorithms that require a higher degree of cooperation from the tested people. This article is a useful exploration for the new methods of refraction detection. The use of this method is conducive to more convenient refractive screening and provides a basis for the realization of non-contact self-service refractive screening. © 2019 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.