首页|基于跨模态共享特征学习的夜间牛脸识别方法

基于跨模态共享特征学习的夜间牛脸识别方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
[目的]解决夜间环境下牛只身份信息难以有效识别的问题,以期为牛只全天候监测提供技术基础.[方法]提出了一种基于跨模态共享特征学习的夜间牛脸识别方法.首先,模型框架采用浅层双流结构,有效提取不同模态的牛脸图像中的共享特征信息;其次,引入Triplet注意力机制,跨维度地捕捉交互信息,以增强牛只身份信息的提取;最后,通过嵌入扩展模块进一步挖掘跨模态身份信息的表征.[结果]本文提出的夜间牛脸识别模型在测试集上的平均精度均值、一阶累积匹配特征值(CMC-1)和五阶累积匹配特征值(CMC-5)分别为90.68%、94.73%和 97.82%,相较于未进行跨模态训练的模型,提高了 19.67、18.91 和 12.00 个百分点.[结论]本研究所提出的模型为夜间牛只身份识别问题提供了可靠的解决方案,为实现牛只全天候持续监测奠定了坚实的技术基础.
Nighttime cattle face recognition based on cross-modal shared feature learning
[Objective]To address the challenge of effectively recognizing cattle identity in the nighttime,and lay the technical foundation for 24-hour monitoring of cattle.[Method]A nighttime cattle face recognition method based on cross-modal shared feature learning was proposed.The model framework adopted a shallow dual-stream structure to effectively extract shared feature information from different modalities of cattle face images.Additionally,a triplet attention mechanism was introduced to capture intermodal interaction information across dimensions,enhancing the extraction of cattle identity information.Finally,an embedded extension module was utilized to further explore the representation of cross-modal identity information.[Result]The nighttime cattle face recognition model proposed in this article achieved a mean average precision,the first order cumulative matching eigenvalue(CMC-1)and the fifth order cumulative matching eigenvalue(CMC-5)of 90.68%,94.73%and 97.82%on the test set,respectively.Compared to the model without cross-modality training,the three indexes improved by 19.67,18.91 and 12.00 percentage points,respectively.[Conclusion]The proposed method provides a reliable solution for nighttime cattle identity recognition,laying a solid technical foundation for the application of continuous 24-hour monitoring of cattle.

CattleIdentificationHeterogeneous face recognitionCross-modalityAttention mechanismShared featureNighttime

许兴时、王云飞、邓红兴、宋怀波

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西北农林科技大学机械与电子工程学院/农业农村部农业物联网重点实验室/陕西省农业信息感知与智能服务重点实验室,陕西杨凌 712100

牛 身份识别 异质面部识别 跨模态 注意力机制 共享特征 夜间

国家重点研发计划国家自然科学基金陕西省农业重点核心技术项目陕西省科技创新引导计划

2023YFD1301800322729312023NYGG0052022QFY11-02

2024

10.7671/j.issn.1001-411X.202403020

华南农业大学学报
华南农业大学

华南农业大学学报

CSTPCD北大核心
影响因子：0.837
ISSN：1001-411X
年,卷(期)：2024.45(5)
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