CNN-Bi-LSTM:一个复杂的以环境为导向的牛行为分类网络,基于CNN和Bi-LSTM的融合
Guohong Gao1, Chengchao Wang1, Jianping Wang1
1School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种新的牛行为分类网络,使用卷积神经网络 (CNN) 和双向长短期记忆 (Bi-LSTM) 进行智能农业. 在复杂的农场环境中,CNN-Bi-LSTM模型实现了94.3%的准确性,超过了其他深度学习方法.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 智能牛养殖需要准确的行为分类,以改善动物福利和管理.
- 现有的方法难以应对复杂的农场环境,变化的光线和堵塞.
研究的目的:
- 开发和验证一个新的深度学习网络,用于在复杂的农业环境中精确地分类牛的行为.
- 为了提高牛行为识别的准确性和通用性,超越传统的单传感器数据分析.
主要方法:
- 开发了一个新的网络,将基于VGG16的CNN用于空间特征提取和Bi-LSTM用于时间语义分析.
- 数据是在真实的农场环境中收集的,定义了八种基本的牛行为.
- 用MASK-RCNN,CNN-LSTM和EfficientNet-LSTM进行了废弃实验,概括评估和比较分析.
主要成果:
- 拟议的CNN-Bi-LSTM模型实现了94.3%的准确性,94.2%的精度和93.4%的回忆.
- 与MASK-RCNN,CNN-LSTM和EfficientNet-LSTM相比,该模型表现出卓越的性能.
- 证实了对不同学科和视角的有效概括.
结论:
- CNN-Bi-LSTM融合有效提取多式联运特征,用于在具有挑战性的环境中强大的牛行为分类.
- 这种方法显著提高了精度和通用性,解决了传统方法的局限性.
- 该技术为农业部门提供了实质性的实用,经济和社会效益.
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