DGS-YOLO:用于快速识别猪脸部的检测网络.
Hongli Chao1, Wenshuang Tu1, Tonghe Liu2
1College of Information Technology, Jilin Agricultural University, Changchun 130118, China.
Animals : an open access journal from MDPI
|January 28, 2026
概括
本研究介绍了DGS-YOLO,这是一个增强的猪面部识别模型,用于提高食品安全和保险准确性. 该模型在复杂的农业环境中实现了卓越的性能,即使数据有限.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 动物科学动物科学
背景情况:
- 对猪来说,面部识别对于食品安全和保险至关重要.
- 现有的方法在复杂的农业环境中由于遮和类似的纹理而难以准确.
研究的目的:
- 开发一个增强的面部识别模型,DGS-YOLO,用于精确识别群体养的年轻猪.
- 在具有挑战性的农业环境中提高识别准确性.
主要方法:
- 基于YOLOv11n提出的DGS-YOLO模型,包括动态卷积 (DMConv),一个C3k2_GBC模块,SimAM注意力和Shape-IoU损失.
- 使用自建数据集进行培训和评估.
主要成果:
- 与YOLOv11n基线相比,DGS-YOLO在准确度上有4%的改善,在回忆中有2.1%的改善,在mAP50中有2.3%的改善.
- 在全面的指标中表现优于Faster R-CNN和SSD.
- 在有限的样本场景中,表现出强烈的概括,具有显著的准确性和mAP50增加 (20.1%和10.3%).
结论:
- DGS-YOLO为复杂环境中的猪面部识别提供了高度准确和强大的解决方案.
- 改进后的模型解决了食品安全和保险领域的实际需求.
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