论文标题
牛虹膜细分的新型深度学习框架
Novel Deep Learning Framework For Bovine Iris Segmentation
论文作者
论文摘要
虹膜分割是确定动物生物识别以建立牲畜的可追溯性系统的第一步。在这项研究中，我们提出了一个新颖的深度学习框架，以使用Bovineaaeeyes80公共数据集使用注释标签，以最少使用注释标签。在实验中，选择具有VGG16骨架的U-NET作为编码器和解码器模型的最佳组合，证明了99.50％的精度和98.35％的骰子系数得分。值得注意的是，所选模型即使没有正确的注释数据也可以准确细分损坏的图像。这项研究有助于进步虹膜细分和可靠的DNNS培训框架的发展。
Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.
