基于改进的YOLOv7x和卡尔曼过器跟踪算法以及视频流媒体的冬季小麦耳数计
Xingmei Xu1, Lei Zhou1, Helong Yu1
1College of Information and Technology, Jilin Agricultural University, Changchun, Jilin, China.
Frontiers in plant science
|July 2, 2024
概括
一个新的YOLOv7xSPD模型在高分辨率的无人机视频中准确地检测和计数小麦穗. 该方法通过实时现场监测,提高了小麦产量预测和农业管理.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的小麦计数对于产量预测,育种和管理至关重要.
- 现有的方法在实时,高分辨率的无人机 (UAV) 视频数据方面遇到了困难.
研究的目的:
- 为无人机视频开发一个准确和实时的小麦耳朵检测和计数模型.
- 改进现有的农业应用深度学习模型.
主要方法:
- 在YOLOv7x深度学习模型中集成了一个空间到深度 (SPD) 模块,创建了YOLOv7xSPD.
- 使用规范化高斯瓦瑟斯坦距离 (NWD) 损失函数用于增强检测.
- 结合YOLOv7xSPD与卡尔曼过器跟踪和匈牙利匹配以进行基于视频的计数 (YOLOv7xSPD计数器).
主要成果:
- YOLOv7xSPD实现了高精度 (95.85%),回忆 (94.71%),F1得分 (95.28%) 和AP (94.99%) 的高精度.
- 与YOLOv7x和其他物体检测模型 (更快的RCNN,SSD,YOLOv5s,YOLOv7) 相比,该模型表现出更高的性能.
- YOLOv7xSPD计数器处理了大约5.5 FPS的高分辨率视频 (3840x2160) ,与地面真相有很高的相关性 (R2 = 0.99).
结论:
- 该YOLOv7xSPD计数器模型提供了一个强大的解决方案,用于从无人机视频中实时计数小麦耳.
- 这项技术为小麦产量预测,遗传育种和优化种植管理提供了宝贵的工具.
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