轻量级的羊面部识别模型结合了分组卷积和参数融合
Gaochao Liu1, Lijun Kang1, Yongqiang Dai1
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
一个新的参数融合轻量级你只看一次 (PFL-YOLO) 模型改善了绵羊的面部识别. 这种轻量级模型在资源有限的设备上提供了高精度,解决了现有技术的局限性.
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:
- 羊面部识别对于个体识别和行为监测至关重要.
- 现有的模型需要大量的计算资源,这阻碍了在移动或嵌入式设备上部署.
- 这导致实际应用中的精度降低和识别时间增加.
研究的目的:
- 开发一种轻量级和高效的羊面部识别模型.
- 在资源有限的设备上克服当前模型的计算和准确性限制.
- 引入基于YOLOv8n架构的改进模型.
主要方法:
- 提出了参数融合轻量级你只看一次 (PFL-YOLO) 模型,这是YOLOv8n的增强.
- 集成高效混合动力车 (EHConv) 和剩余C2f (RC2f) 模块,以改善特征提取和多尺度融合.
- 开发了一个参数融合检测 (PFDetect) 模块,以减少模型参数和计算复杂性.
主要成果:
- PFL-YOLO实现了99.5%的mAP@50和87.4%的mAP@50:95的性能效率平衡.
- 该模型的参数仅为1.01M,大小为2.1MB,大大降低了计算负载.
- 与各种轻型模型相比,参数数量和模型大小减少了高达83.7%和82.5%.
结论:
- 该PFL-YOLO模型为羊的面部识别提供了高精度和高效率.
- 它的轻量级性质使其适合在资源有限的设备上部署.
- PFL-YOLO为先进的绵羊监控系统提供了一个可行的新解决方案.
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