通过深度学习识别口模式图像来识别个别的牛
Taejun Lee1, Youngjun Na1,2, Beob Gyun Kim1
1Department of Animal Science, Konkuk University, Seoul 05029, Republic of Korea.
Animals : an open access journal from MDPI
|September 28, 2023
概括
这项研究使用了Hanwoo牛口腔图像的深度学习来进行个人识别. 最好的模型达到了97%的准确性,显示了口模式对牛的可追溯性有效.
科学领域:
- 动物科学动物科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 个人识别对于畜牧管理和可追溯性至关重要.
- 传统的牛标识方法可能是劳动密集型的,容易出现错误.
- 深度学习为准确的动物识别提供了潜在的自动化解决方案.
研究的目的:
- 开发和评估一个深度学习模型,用于识别使用口腔图像识别单个Hanwoo牛.
- 评估不同的Efficientnet v2模型大小和优化器对此任务的有效性.
- 为了确定口图案作为汉武牛的唯一标识符的潜力.
主要方法:
- 收集了来自336只汉武牛的9230张嘴巴图像,每个人的图像多个.
- 使用YOLO v8进行喷口图像裁剪,并为训练,验证和测试准备数据.
- 使用Efficientnet v2 (tiny, small, medium) 和各种优化器 (SGD,RMSProp,Adam,Lion) 的应用转移学习.
主要成果:
- 带有狮子优化器的Efficientnet v2小型模型实现了最高的验证准确性 (0.981).
- 带有Adam优化器的Efficientnet v2小型模型显示出最好的测试准确度 (0.970).
- 使用RMSProp优化器的模型显示了最小的重复错误,表明了稳定性.
结论:
- 使用口腔图像的深度学习模型可以准确地识别单个Hanwoo牛.
- 嘴巴的图案可以作为一种可靠的生物识别密钥来识别牛.
- 这项技术在提高牲畜可追溯性和管理系统方面具有重大潜力.
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