The Use of Synthetic Data to Facilitate Eye Segmentation Using Deeplabv3+

Annals of Emerging Technologies in Computing

article

Melih Öz¹  Danisman, Taner¹  Melih Günay¹  Sanal, Esra Zekiye²  Özgür Duman²  Ledet, Joseph William¹ 

[1] Akdeniz University, Faculty of Engineering, Computer Engineering Department;Akdeniz University, School of Medicine, Department of Pediatric Neurology

关键词: Deep Neural Networks;    Eye Segmentation;    Image Augmentation;    Synthetic Data;   

DOI  :  10.33166/AETiC.2021.03.001

学科分类：电子与电气工程

来源: International Association for Educators and Researchers (IAER)

【 摘 要 】

The human eye contains valuable information about an individual’s identity and health. Therefore, segmenting the eye into distinct regions is an essential step towards gathering this useful information precisely. The main challenges in segmenting the human eye include low light conditions, reflections on the eye, variations in the eyelid, and head positions that make an eye image hard to segment. For this reason, there is a need for deep neural networks, which are preferred due to their success in segmentation problems. However, deep neural networks need a large amount of manually annotated data to be trained. Manual annotation is a labor-intensive task, and to tackle this problem, we used data augmentation methods to improve synthetic data. In this paper, we detail the exploration of the scenario, which, with limited data, whether performance can be enhanced using similar context data with image augmentation methods. Our training and test set consists of 3D synthetic eye images generated from the UnityEyes application and manually annotated real-life eye images, respectively. We examined the effect of using synthetic eye images with the Deeplabv3+ network in different conditions using image augmentation methods on the synthetic data. According to our experiments, the network trained with processed synthetic images beside real-life images produced better mIoU results than the network, which only trained with real-life images in the Base dataset. We also observed mIoU increase in the test set we created from MICHE II competition images.

【 授权许可】

CC BY