在农业场景中,基于改善的轻量YOLOv8的小牛行为识别研究
Ze Yuan1, Shuai Wang1, Chunguang Wang1
1College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals : an open access journal from MDPI
|March 28, 2025
概括
这项研究使用改进的YOLOv8模型与P2小目标检测层和灯光修剪来增强小牛的行为识别. 优化的模型实现了高精度 (90.9% mAP) 和更小的尺寸,使复杂环境中的实时监控成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 动物行为分析 动物行为分析
- 机器学习 机器学习
背景情况:
- 准确识别小牛的行为对于畜牧管理至关重要.
- 复杂的环境因素,如遮蔽和不同的光线条件对现有模型构成挑战.
- YOLOv8模型提供了坚实的基础,但需要优化,以便在具有挑战性的场景中实现实时,高精度的性能.
研究的目的:
- 开发一个改进的YOLOv8模型,以准确高效地识别小牛的行为.
- 为了提高模型在复杂场景中的表现,重叠,遮蔽和多样化的照明.
- 为了创建一个轻量级,高精度和强大的模型,用于实时,全天的小牛行为监测.
主要方法:
- 采用了2918张图像的小牛每日行为数据集.
- 通过整合P2小目标检测层,YOLOv8模型得到了增强.
- 应用了Lamp修剪策略,以减少计算复杂性和模型大小.
- 性能与包括SSD,YOLOv5n和其他YOLOv8变体在内的模型进行了比较.
主要成果:
- 改进的模型实现了0.949M参数,4.0G FLOP和2.3Mb大小的90.9%的平均平均精度 (mAP).
- 在白天 (85.1%) 和夜间 (84.8%) 条件下保持高mAP.
- 在不同阴影级别 (轻度,中度,重度) 中实现了平均87.3%的mAP.
结论:
- 增强的YOLOv8模型具有P2检测和灯光修剪,可显著提高小牛的行为识别准确性和效率.
- 该模型在复杂,可变的环境条件下展示了强度和实时能力.
- 这项研究为在实际农业环境中持续全天监测小牛行为提供了有价值的工具.
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