针对瞳孔区域屈光度识别准确率低、检测效率低等问题, 本文提出一种基于改进Y0L0v3深度 神经网络的瞳孔图像检测算法。首先构建用于提取瞳孔主特征的二分类检测网络Y0L0v3-base,强化 对瞳孔特征的学习能力。然后通过迁移学习, 将训练模型参数迁移至 YOLOv3-DPDC(Deep Pupil Diopter Classify), 降低样本数据分布不均衡造成的模型训练困难以及检测性能差的难题, 最后采用 Fine-tuning调参快速训练YOLOv3多分类网络, 实现了对瞳孔屈光度快速检测。通过采集的1200张 红外瞳孔图像进行实验测试, 结果表明本文算法屈光度检测准确率达91.6%, 检测速度可达 45 fps, 优于使用Faster R-CNN进行屈光度检测的方法。

To address the problems of low diopter recognition accuracy and low detection efficiency in the pupil area, this paper proposes a pupil image detection algorithm based on an improved YOLOv3 deep neural network. First, a two-class detection network YOLOv3 base for extracting the main features of the pupil is constructed to strengthen the learning ability of the pupil characteristics. Subsequently, through migration learning, the training model parameters are migrated to YOLOv3-DPDC to reduce the difficulty of model training and poor detection performance caused by the uneven distribution of sample data. Finally, fine-tuning is used to quickly train the YOLOv3 multi-classification network to achieve accurate pupil diopter detection. An experimental test was performed using the 1200 collected infrared pupil images. The results show that the average accuracy of diopter detection using this algorithm is as high as 91.6%, and the detection speed can reach 45 fps; these values are significantly better than those obtained using Faster R-CNN for diopter detection.