关于研究联合YOLOv5-DeepSort检测和跟踪算法用于Rhynchophorus ferrugineus的研究
Shuai Wu1,2, Jianping Wang1, Wei Wei2
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Insects
|February 26, 2025
概括
结合YOLOv5和DeepSort的新算法有效地检测和跟踪红棕虫 (RPW),这是一种破坏性的棕虫害. 这种增强的系统提高了害虫控制效率和准确性在现场应用.
科学领域:
- 农业昆虫学 农业昆虫学
- 计算机视觉 计算机视觉
- 虫害管理 虫害管理 虫害管理
背景情况:
- 红棕虫 (RPW), *Rhynchophorus ferrugineus*,对棕健康构成重大威胁,经常导致植物完全死亡.
- 有效和高效的控制策略对于缓解RPW损害至关重要.
研究的目的:
- 开发和评估用于红棕虫 (RPW) 控制的新型检测和跟踪算法.
- 提高害虫监测系统的精度和效率.
主要方法:
- 开发了一种改进的YOLOv5模型,包括一个小物体检测层和一个注意力机制.
- 通过将增强的YOLOv5集成为其探测器并引入历史数据模块来修改DeepSort算法.
- 进行了实验验证,以评估联合YOLOv5-DeepSort算法的性能.
主要成果:
- 改进的YOLOv5探测器实现了90.1%的平均精度 (mAP@.5) 和93.8%的精度 (P).
- 联合YOLOv5-DeepSort算法显示多个对象跟踪精度 (MOTA) 为94.3%,多个对象跟踪精度 (MOTP) 为90.14%.
- 该算法减少了33.3%的目标身份交换机,并实现了94.1%的计数精度.
结论:
- 联合开发的YOLOv5-DeepSort算法显著提高了红棕虫检测和跟踪的准确性和效率.
- 改进的算法满足了实时现场监测和控制RPW感染的实际要求.
- 这一进步为针对破坏性棕树害虫的综合防治策略提供了一个有前途的工具.
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