基于空间时空特征学习的无多头肉的行为识别

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Yilei Hu¹, Jiaqi Xiong¹, Jinyang Xu¹

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P. R. China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, P. R. China.

Poultry science

|September 26, 2024

概括

这项研究引入了一种自动化系统,用于识别视频中的多种肉行为,改善家禽福利和疾病检测. 肉行为识别系统 (BBRS) 使用时空学习进行准确的实时分析.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
动物行为动物行为

背景情况:

禽类的行为对健康,福利和生产至关重要.
现有的多对象行为识别方法经常忽略时间视频特征,导致不准确.
及时的行为数据可以提高福利和减少肉的疾病传播.

研究的目的:

开发一个端到端的系统,用于识别视频中多个无肉的同时行为.
利用时空特征学习来提高肉行为识别的准确性.
为了提供一个高效的工具,自动,准确分析肉的行为.

主要方法:

提出了肉行为识别系统 (BBRS),集成了改进的YOLOv8s探测器,Bytetrack跟踪器和3D-ResNet50-TSAM模型.
增强了YOLOv8s与MPDIoU用于多个肉的识别,并使用Bytetrack来跟踪单个肉.
使用3D-ResNet50-TSAM模型与时空注意模块来学习视频序列中的时空特征.

主要成果:

改进的YOLOv8s探测器实现了99.50%的mAP@0.5.
在不同阻塞水平下,Bytetrack追踪器获得了93.89%的平均MOTA.
3D-ResNet50-TSAM模型表现出高性能,准确度,精度,回忆和F1得分高于97.65%.

结论:

该BBRS有效地识别使用时空特征学习的多个同时肉的行为.
该系统即使在不完整的视频序列 (26) 中也表现出强大的性能,达到93.98%的准确性.
这项研究为自动和准确监测无肉行为提供了有价值的工具.

关键词:

行为识别行为识别行为识别 brojler 肉肉是一个计算机视觉计算机视觉终端到终端的终端.时间空间的特征.

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