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一个新的轻量级框架,用于在密集农业中非接触式肉面部识别

Bin Gao^1,2, Yongmin Guo¹, Pengshen Zheng²

¹Key Laboratory of Smart Breeding (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Tianjin 300384, China.

Sensors (Basel, Switzerland)

|July 12, 2025

概括

这项研究介绍了YOLO-IFSC,这是一种用于精密肉养殖的新型面部识别系统. 它可以有效地识别个体,加强动物福利监测和农场管理.

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:

精密肉养殖需要有效的个体识别,以便有效管理和福利监测.
传统和现有的基于CNN的方法在密集的农业环境中面临局限性,特别是在遮蔽和计算效率方面.

研究的目的:

开发一个高精度,轻量级的面部识别框架 (YOLO-IFSC),适用于密集的肉养殖.
改进多尺度特征提取,减少计算冗余,增强空间特征聚合,并强调关键的面部区域.

主要方法:

该研究建立在YOLOv11n架构的基础上,整合了四个专门的模块:Inception-F用于多尺度特征,C2f-Faster用于计算效率,SPPELANF用于阻塞处理,以及CBAM用于注意力.
一个自我构建的数据集被用于实验评估.

主要成果:

YOLO-IFSC 实现了 91.5% 的平均平均精度 (mAP@0.5).
该框架显示,与基线相比,参数减少了40.8%,浮点操作 (FLOP) 减少了24.2%.
实时推断速度保持在每秒 36.6 (FPS).

结论:

YOLO-IFSC提供了一种具有成本效益的非接触式解决方案,用于精密养殖中的肉面部识别.
拟议的框架大大提高了个人跟踪和福利监测能力.

关键词:

brojler 面部识别系统面部识别系统轻量级的卷积神经网络是一种神经网络.多级特征聚变的多级特征聚变精准畜牧业精准畜牧业精准畜牧业精准畜牧业

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