在超声波图像中基于深度学习的妊娠囊检测,使用修改后的YOLOv7-E6E模型
Tae-Kyeong Kim1, Jin Soo Kim2, Hyun-Chong Cho3
1Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon 24341, Korea.
Journal of animal science and technology
|June 19, 2023
概括
这项研究利用人工智能增强了母猪怀孕诊断. 通过优化YOLOv7-E6E模型的新功能和升级样本,研究人员提高了妊娠囊检测的准确性,提高了农场的生产力.
科学领域:
- 农业技术 农业技术
- 在畜牧业的人工智能.
- 计算机视觉用于畜牧管理.
背景情况:
- 全球人口和收入的增长推动了对肉类的需求增加.
- 越来越少的农场数量和农民威胁到肉类供应.
- 信息和通信技术 (ICT) 为畜牧场效率提供解决方案.
研究的目的:
- 开发一种人工智能系统,通过超声波图像精确计算母猪妊娠囊.
- 通过精确的早期怀孕检测来提高母猪的生产力.
- 为了提高YOLOv7-E6E模型的性能,用于这个特定的应用.
主要方法:
- 使用了YOLOv7-E6E物体检测模型.
- 从SiLU修改了激活函数,变成SiLU+Mish多激活函数.
- 实现了双立方上采样,而不是最近邻近的上采样.
- 应用自动增大用于数据增大.
主要成果:
- 原来的YOLOv7-E6E模型实现了86.3%的平均平均精度 (mAP).
- 在多重激活功能 (+0.3%),双立方上样 (+0.9%) 和自动增强 (+0.9%) 中,观察到个体改善.
- 同时应用所有拟议的方法导致性能显著提高,达到89.8% mAP (+3.5%).
结论:
- 优化的YOLOv7-E6E模型显著提高了母猪妊娠囊检测的准确性.
- 这些人工智能驱动的怀孕诊断方面的改进可以提高牲畜农场的生产率.
- 该研究表明,先进的信息和通信技术在应对现代畜牧业的挑战方面具有潜力.
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