APPLICATION OF THE YOLOV5 NEURAL NETWORK MODEL TO INCREASE THE ACCURACY OF IRIS AND PUPIL IMAGE SEGMENTATION
Authors:
1.
Kazan National Research Technical University named after A.N. Tupolev
(Information Security Systems Department, docent)
Type:
Article
Pages:
from 136
to 140
Status:
In work
Language:
Russian
Keywords:
HUMAN FUNCTIONAL STATE, DRIVER FATIGUE STATE, PUPILLOMETRY, NEURAL NETWORKS, IRIS AND PUPIL SEGMENTATION, TIME SERIES SMOOTHING, COMPUTER VISION, NEURAL NETWORK MODEL YOLOV5
Abstract (English):
This article presents the application of a new approach to segmentation of images of the iris and pupil of the human eye using the pretrained neural network model YoLoV5. Despite the use of modern neural network technologies and computer vision methods for image processing and eye segmentation, not all of them guarantee obtaining a highly accurate result of the desired circles. Examples of incorrect segmentation of the iris and pupil obtained using the Mediapipe framework and the FloodFill algorithm are given. Smoothing filters were previously used to eliminate artifacts, noise and outliers in the data. At the same time, the use of smoothing filters contributes to the distortion of the data they process, especially when working in real-time systems with the property of observation duration. Since the diagnosis of the functional state of a person by the pupillometry method is sensitive to the quality of the original data, the use of smoothing filters in a number of diagnostic cases is not acceptable. To improve the accuracy of iris and eye image segmentation and avoid data distortion by smoothing filters, the authors developed a computer vision module based on the eye circumference segmentation mechanism using the pre-trained YoLoV5 neural network model. Examples of eye circumference segmentation by the YoLoV5 neural network are given. The accuracy of iris and pupil image segmentation was assessed by comparing the number of outliers and omissions in time series values obtained using the approaches discussed in the article. The approach to using the YoLoV5 neural network model proposed in the article is implemented in solving the problem of driver fatigue monitoring and is used in a prototype of a specialized device developed by the authors.
This article presents the application of a new approach to segmentation of images of the iris and pupil of the human eye using the pretrained neural network model YoLoV5. Despite the use of modern neural network technologies and computer vision methods for image processing and eye segmentation, not all of them guarantee obtaining a highly accurate result of the desired circles. Examples of incorrect segmentation of the iris and pupil obtained using the Mediapipe framework and the FloodFill algorithm are given. Smoothing filters were previously used to eliminate artifacts, noise and outliers in the data. At the same time, the use of smoothing filters contributes to the distortion of the data they process, especially when working in real-time systems with the property of observation duration. Since the diagnosis of the functional state of a person by the pupillometry method is sensitive to the quality of the original data, the use of smoothing filters in a number of diagnostic cases is not acceptable. To improve the accuracy of iris and eye image segmentation and avoid data distortion by smoothing filters, the authors developed a computer vision module based on the eye circumference segmentation mechanism using the pre-trained YoLoV5 neural network model. Examples of eye circumference segmentation by the YoLoV5 neural network are given. The accuracy of iris and pupil image segmentation was assessed by comparing the number of outliers and omissions in time series values obtained using the approaches discussed in the article. The approach to using the YoLoV5 neural network model proposed in the article is implemented in solving the problem of driver fatigue monitoring and is used in a prototype of a specialized device developed by the authors.
Keywords:
HUMAN FUNCTIONAL STATE, DRIVER FATIGUE STATE, PUPILLOMETRY, NEURAL NETWORKS, IRIS AND PUPIL SEGMENTATION, TIME SERIES SMOOTHING, COMPUTER VISION, NEURAL NETWORK MODEL YOLOV5
HUMAN FUNCTIONAL STATE, DRIVER FATIGUE STATE, PUPILLOMETRY, NEURAL NETWORKS, IRIS AND PUPIL SEGMENTATION, TIME SERIES SMOOTHING, COMPUTER VISION, NEURAL NETWORK MODEL YOLOV5
