外文摘要：Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel -wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U -Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.
外文关键词：deep learning;segmentation;identification;Cow;Iris
作者：Yoon, Heemoon;Park, Mira;Lee, Hayoung;An, Jisoon;Lee, Taehyun;Lee, Sang-Hee
作者单位：Kangwon Natl Univ;Univ Tasmania
期刊名称：JOURNAL OF ANIMAL SCIENCE AND TECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：66（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台