卡姆拉-YOLOv8n:牛行为识别基于改进的YOLOv8n
Qingxiang Jia1, Jucheng Yang1, Shujie Han2,3
1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300453, China.
Animals : an open access journal from MDPI
|October 26, 2024
概括
一个新的CAMLLA-YOLOv8n模型准确地识别了荷尔斯坦牛的行为,如放牧和,改善了农场动物的监测. 这种人工智能进步提高了检测各种牛状态的精度和回忆力,以实现更智能的畜牧业.
科学领域:
- 计算机视觉和机器学习在农业中的应用
- 动物行为分析.
- 精准畜牧业 精准畜牧业 精准畜牧业 精准畜牧业
背景情况:
- 监测牛的行为对于健康评估和农场管理至关重要.
- 在现实条件下,准确检测像放牧,站立和的行为是具有挑战性的.
- 现有的模型需要优化,以提高农业环境中的性能.
研究的目的:
- 开发一种先进的深度学习模型,CAMLLA-YOLOv8n,用于准确地识别荷尔斯坦牛的行为.
- 通过注意力机制和改进的特征提取来增强YOLOv8n架构,以便更好地检测.
- 为了验证模型的有效性,对荷尔斯坦牛行为综合数据集进行验证.
主要方法:
- 拟议的CAMLLA-YOLOv8n模型整合了协调注意力 (C2f-CA) 和MLLA注意力机制.
- 改进的SPPF模块 (SPPF-GPE) 用于增强小目标识别.
- 利用Shape-IoU损失来改进边界框匹配,并使用混合数据增强.
主要成果:
- 与YOLOv3-tiny,YOLOv5n/s,YOLOv7-tiny,YOLOv8n/s.相比,CAMLLA-YOLOv8n表现出更高的精度,这也是因为它们具有更高的精度.
- 在精度 (2.18%),回忆 (1.62%),mAP@0.5 (1.84%) 和mAP@0.5:0.95 (1.77%) 中比YOLOv8n.实现了显著的改进.
- 该模型有效地检测到荷尔斯坦牛的行为,包括放牧,站立,躺着,,和战斗.
结论:
- 在农业环境中,CAMLLA-YOLOv8n模型提供了准确和快速的霍尔斯坦牛行为识别.
- 这一进步支持畜牧业的数字化和智能化转型.
- 改善牛的行为监测可以带来更好的农场经济效益和动物福利.
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