基于改进的YOLOv7和DeepSort的视频计算小麦耳的方法研究
Tianle Wu1, Suyang Zhong1, Hao Chen1
1College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种改进的YOLOv7和DeepSort方法,用于使用无人机视频自动计数小麦耳朵. 改进后的模型实现了高精度,使得在大型田间能够高效地进行产量估计.
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:
- 准确的小麦产量估计依赖于计数小麦穗,由于密度和封闭,在大田中这是一项具有挑战性的任务.
- 使用静态图像的传统方法存在局限性;本研究探讨了基于视频的动态计数以提高效率.
研究的目的:
- 开发一种使用无人机 (UAV) 视频在大型田地计算小麦穗的自动化和准确方法.
- 增强对象检测和多对象跟踪算法,以提高小麦计数性能.
主要方法:
- 优化了YOLOv7对象检测模型的多维动态卷积 (ODConv),全球上下文网络 (GCNet) 和协调注意力 (CA) 机制.
- 改进了DeepSort多对象跟踪算法,通过集成修改后的ResNet特征提取器来实现更好的小麦耳重新识别.
- 利用改进的YOLOv7-DeepSort模型,根据在无人机视频数据中跟踪的独特对象ID计算小麦穗.
主要成果:
- 增强的YOLOv7模型实现了96.2%的平均平均精度 (mAP),比原始模型提高了2.5%.
- 改进的YOLOv7-DeepSort模型显示多个对象跟踪精度 (MOTA) 为75.4%.
- 使用无人机方法计数小麦耳的准确度在95%至98%之间,平均L1损失为4.2.
结论:
- 提出的基于YOLOv7和DeepSort的方法有效地检测和跟踪无人机视频中的小麦穗,以进行准确的计数.
- 这种方法提供了一个高效和准确的解决方案,用于估计大规模农田的小麦产量.
- 集成先进的深度学习技术显著提高了自动化农业监测系统的性能.
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