基于深度学习和双线特征融合的羊面部识别模型
Zhuang Wan1, Fang Tian1,2,3, Cheng Zhang1,2,3
1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Animals : an open access journal from MDPI
|June 28, 2023
概括
准确的绵羊识别对于数字农业至关重要. 使用RepVGG和双线特征的新深度学习模型增强了绵羊的面部识别,尽管姿势变化,其准确性超过99%.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 准确的个体绵羊识别对于数字化绵羊养殖和精准畜牧业至关重要.
- 羊面部识别的挑战包括姿势和拍摄角度的变化,影响准确性.
研究的目的:
- 开发一个强大的深度学习模型,用于准确的羊面部识别.
- 为了减轻姿势和角度变化对识别准确性的影响.
主要方法:
- 提出了一个深度学习模型,集成RepVGG算法和双线特征提取和融合.
- 设计了一个具有注意力机制的特征提取通道和RepVGG块,以实现高效,无损的模型压缩.
- 使用双线特征提取网络和相同尺度的特征融合来提高识别稳定性.
主要成果:
- 该模型实现了高识别率:95.95% (侧面),97.64% (正面) 和99.43% (全面).
- 显著降低了羊脸对识别准确性的影响.
- 超过了现有的几种最先进的羊面部识别模型.
结论:
- 拟议的深度学习模型有效地解决了由姿势和角度变化引起的羊面部识别方面的挑战.
- 这种方法增强了个体绵羊的识别,支持精准畜牧业和数字绵羊养殖的进步.
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