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增强的YOLOv8用于在复杂的农业环境中强大的猪检测和计数

Jian Li^1,2, Wenkai Ma^3,4, Yanan Wei²

¹School of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, China.

Animals : an open access journal from MDPI

|July 29, 2025

概括

使用新型EAPC-YOLO模型实现了精确的猪计数,提高了精确的畜牧业. 这种方法提高了在具有挑战性的农场环境中检测和计数的准确性.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
动物科学动物科学

背景情况:

精密畜牧业需要精确的猪计数来管理.
现有的检测方法在农场中与阻塞,照明和密度变化作斗争.
复杂的农业环境给自动化猪监测带来了挑战.

研究的目的:

开发一个强大的猪计数系统,用于精确的畜牧业.
克服目前基于检测的计数方法在不同农业环境中的局限性.
通过改进对象检测和计数精度来增强自动监控.

主要方法:

开发了一种增强的YOLOv8网络 (EAPC-YOLO),使用DCNv4,BiFPN,EfficientViT和PIoU v2损失.
集成了一个密度感知后处理模块,具有自适应性非最大抑制 (NMS).
该架构将高级检测与密度意识策略相结合,以改进特征提取和定位.

主要成果:

EAPC-YOLO在检测方面实现了94.2%的mAP@0.5和96.8%的计数精度.
与最强的基线 (YOLOv11n) 相比,表现出12.3%和15.7%的显著改善.
在各种场景中验证,包括夜间,室内和白天,猪密度各不相同.

结论:

拟议的EAPC-YOLO模型在具有挑战性的农业环境中提供了强大而准确的猪计数.
这项技术支持优化养,健康监测和整体精确畜牧管理.
改进的架构和后处理模块显著提升了自动化农场动物监控.

关键词:

这就是YOLOv8的意义.计算机视觉计算机视觉密度感知计数方式猪检测系统猪检测系统精准畜牧业精准畜牧业精准畜牧业精准畜牧业

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