一个注意力剩余特征融合机制用于羊面部识别
Yue Pang1, Wenbo Yu1, Yongan Zhang2
1College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China.
Scientific reports
|October 10, 2023
概括
这项研究引入了注意力残留模块 (ARM),用于改进智能牧场的羊面部识别. 这种新方法提高了卷积神经网络 (CNN) 的准确性,有助于监测羊的行为和预防疾病.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 智能牧场的发展面临着在绵羊行为监测,疾病预防和管理方面的挑战.
- 传统的羊识别方法效率低下,劳动密集.
- 标准卷积神经网络 (CNN) 在特定任务的特征提取中扎,以进行羊识别.
研究的目的:
- 开发一种使用面部识别来识别单个绵羊的改进方法.
- 为了提高羊面部识别CNN的特征提取能力.
- 解决智能牧场中现有的羊识别技术的局限性.
主要方法:
- 建议使用注意力残留模块 (ARM) 来汇总跨CNN层的特征映射.
- 创建了一个由38只绵羊的4490张图像组成的羊面部识别数据集.
- 使用图像增强技术 (旋转,滑动) 来扩展数据集.
- 该ARM与VGG16,GoogLeNet和ResNet50网络进行了集成.
主要成果:
- ARM使VGG16的准确度提高了10.2%,谷歌网络提高了6.65%,ResNet50提高了4.38%.
- 增强的CNN模型在羊面部识别方面表现出卓越的表现.
- 提出的方法在特定的羊面部识别任务中被证明是有效的.
结论:
- 注意剩余模块显著提高了CNN在羊面部识别方面的性能.
- 这种方法为智能牧场中的单个绵羊识别提供了可行的解决方案.
- 开发的方法支持改善绵羊管理,疾病预防和监测.
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