Pupil Diopter Detection Approach Based on Improved YOLOv3

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.