Novel Deep Learning Framework For Bovine Iris Segmentation
- URL: http://arxiv.org/abs/2212.11439v1
- Date: Thu, 22 Dec 2022 01:15:08 GMT
- Title: Novel Deep Learning Framework For Bovine Iris Segmentation
- Authors: Heemoon Yoon, Mira Park, Sang-Hee Lee
- Abstract summary: We propose a novel deep learning framework for pixel-wise segmentation 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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