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基于改进的YOLOv7与DeepSORT结合的动态猪计数方法的研究
Xiaobao Shao1, Chengcheng Liu1, Zhixuan Zhou1
1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Animals : an open access journal from MDPI
|April 27, 2024
概括
这项研究引入了一种改进的YOLOv7模型与DeepSORT,用于在复杂环境中准确,实时的猪计数. 改进的模型实现了高精度和速度,这对于自动化大规模农业至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 准确的猪库存对于精准农业至关重要.
- 在复杂的猪舍中,自动计数具有挑战性,因为存在障碍和猪的行为.
- 现有的深度学习方法通常依赖于静态图像或上空视图,限制了现实世界的应用.
研究的目的:
- 开发一种基于视频的可靠动态计数方法,用于复杂环境中的猪.
- 增强YOLOv7对象检测模型,以提高猪计数的准确性和效率.
- 将YOLOv7与DeepSORT集成,用于实时跟踪和计数.
主要方法:
- 优化了使用PConv的YOLOv7架构,以减少计算和提高推理速度.
- 集成的协调注意力 (CA) 机制,以提高角和强度的感知.
- 结合了增强的YOLOv7和DeepSORT,用于基于视频的动态猪计数.
主要成果:
- 与原始模型相比,改进的YOLOv7在斜面,顶部和组合数据集中实现了更高的mAP.
- 在目标检测中表现出优越的性能与YOLOv5,YOLOv4,YOLOv3,更快的RCNN和SSD相比.
- 在动态计数实验中,YOLOv7-DeepSORT系统在22 FPS中实现了96.58%的平均精度.
结论:
- 拟议的动态计数方法有效地解决了复杂环境中自动猪计数的挑战.
- 增强的YOLOv7-DeepSORT模型为大规模养殖中实时,准确的猪库存提供了可行的解决方案.
- 这项研究提供了有价值的数据,并为推进自动猪计数技术提供了参考.
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