羊脸网 (SheepFaceNet):一种速度准确度平衡的模型,用于羊的面部识别.
Xiaopeng Li1, Yichi Zhang1, Shuqin Li1
1College of Information Engineering, Northwest A&F University, Xianyang 712100, China.
Animals : an open access journal from MDPI
|June 28, 2023
概括
一个新的轻量级模型,SheepFaceNet,增强了羊面部识别,用于智能农业. 它平衡速度和准确性,提高了个别羊的识别效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 农业技术 农业技术
背景情况:
- 单个绵羊的识别对于智能农业至关重要,但目前的计算机视觉模型缓慢且难以部署.
- 现有的羊面部识别系统面临着参数大小和处理速度缓慢的挑战.
研究的目的:
- 为智能农业应用开发一个高效,快速的轻量级绵羊面部识别模型.
- 解决当前模型在速度,准确性和部署方面的局限性.
主要方法:
- 提出了一个高效快速的基本模块 (Eblock) 来构建SheepFaceNet模型.
- 开发了使用 Eblock,BiFPN 和优化的网络结构进行检测的 SheepFaceNetDet.
- 开发了SheepFaceNetRec,用于使用Eblock,ECA频道注意力和多尺度特征融合进行识别.
主要成果:
- SheepFaceNet实现了每秒387张绵羊面部图像的识别速度.
- 该模型在自建数据集上显示了97.75%的高准确率.
- 在识别速度和准确性之间实现了最佳平衡.
结论:
- 羊脸网为羊的面部识别提供了一种高效准确的解决方案.
- 该模型的轻量级设计有助于在智能农业环境中部署.
- 这项研究预计将推动在绵羊生产中采用深度学习.
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