Pupil Diopter Detection Approach Based on Improved YOLOv3

LI Yueyi; DING Hongchang; ZHANG Lei; ZHAO Changfu; ZHANG Shibo; WANG Aijia

Volume 44 Issue 7

Jul. 2022

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Infrared Technology > 2022 > 44(7): 702-708.

LI Yueyi, DING Hongchang, ZHANG Lei, ZHAO Changfu, ZHANG Shibo, WANG Aijia. Pupil Diopter Detection Approach Based on Improved YOLOv3[J]. Infrared Technology , 2022, 44(7): 702-708.

Citation:

LI Yueyi, DING Hongchang, ZHANG Lei, ZHAO Changfu, ZHANG Shibo, WANG Aijia. Pupil Diopter Detection Approach Based on Improved YOLOv3[J]. Infrared Technology , 2022, 44(7): 702-708.

Citation:

LI Yueyi, DING Hongchang, ZHANG Lei, ZHAO Changfu, ZHANG Shibo, WANG Aijia. Pupil Diopter Detection Approach Based on Improved YOLOv3[J]. Infrared Technology , 2022, 44(7): 702-708.

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Pupil Diopter Detection Approach Based on Improved YOLOv3

1.
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
2.
Chongqing Research Institute, Changchun University of Science and Technology, Chongqing 401135, China
3.
School of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
4.
Zhengzhou Tobacco Research Institute China National Tobacco Corporation, Zhengzhou 450001, China

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Received Date: August 26, 2021
Revised Date: November 28, 2021

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Abstract

Abstract

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.
- pupil diopter detection,
- deep learning,
- YOLOv3,
- multi-scale features,
- machine vision

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