增强的波形卷积和少数射击原型驱动框架用于Holstein牛的增量识别
Weijun Duan1,2,3, Fang Wang1,2, Honghui Li1,2
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一个新的框架来识别个别的霍尔斯坦牛,提高了智能农场新动物的准确性和稳定性. 该方法增强了特征提取,并使用原型网络进行稳健的增量识别.
科学领域:
- 农业技术
- 计算机视觉
- 机器学习
背景情况:
- 对于智能农场管理来说, 个别的霍尔斯坦牛的识别是至关重要的.
- 现有的识别模型与新动物和外观变化作斗争, 限制了实际应用.
- 目前的开放式方法缺乏稳定性来识别新型个体.
研究的目的:
- 开发一个强大的,增量识别框架为霍尔斯坦牛.
- 在小样本条件下实现新个体的稳定识别.
- 提高牛的识别系统在繁殖场景中的实用性.
主要方法:
- 设计了ResWTA,一个结合波形卷积和空间注意力的特征提取网络.
- 构建了几次拍摄的增强原型网络以增加识别稳定性.
- 评估了各种损失函数,原型计算方法和距离指标.
主要成果:
- ResWTA实现了97.43%的顶级准确度和99.54%的顶级准确度.
- 增强的原型网络提高了4.77%的顶级准确性.
- 综合框架达到94.33%的准确性,减少了4.89%的增量学习遗忘.
结论:
- 拟议的框架允许稳定,增量识别荷尔斯坦牛,即使样本大小小.
- 该网络和增强型原型网络显著提高了识别的稳定性和准确性.
- 这为智能农场管理和牛养殖计划提供了有效的技术支持.
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