基于改进的DeepSort和优化Yolov5s的绿色胡果实计数
Pengcheng Du1, Shang Chen1, Xu Li1
1College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China.
Frontiers in plant science
|July 31, 2024
概括
这项研究引入了一种改进的物体检测模型 (CS_YOLOv5s),用于准确计算绿色胡. 优化的跟踪算法显著提高了精度,并减少了产量估计中的错误.
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:
- 准确的绿胡产量估计对于收获和储存规划至关重要.
- 检测绿胡是具有挑战性的,因为它们的颜色类似于叶子和频繁的遮.
研究的目的:
- 开发一种使用对象检测和多对象跟踪的绿色胡果子自动计数方法.
- 为了提高绿胡产量估计的准确性和效率.
主要方法:
- 一个新的CS_YOLOv5s模型被设计用于绿色胡的检测,结合了带有GSConv和CBAM注意力机制的Slim-Neck.
- 使用SportsTrack的外观匹配和轨道优化优化了DeepSort算法,以改进多对象跟踪.
- 使用mAP,精度,回忆,检测时间,MOTA,MOTP,ID开关,ACP,MAE和RMSE等指标来评估性能.
主要成果:
- CS_YOLOv5s模型实现了98.96%的mAP,95%的精度和97.3%的回忆,检测时间为6.3毫秒,性能优于YOLOv5s.
- 优化DeepSort将ID开关减少了29.41%,并改善了绿胡计数,达到95.33%的ACP,3.33 MAE和3.74 RMSE.
- 与YOLOv5s相比,CS_YOLOv5s模型在与优化DeepSort算法集成时表现出更高的计数精度和稳定性.
结论:
- CS_YOLOv5s模型和优化的DeepSort算法为自动绿色胡计数提供了有效的解决方案.
- 这种方法提高了农业产量估计的准确性和效率,有助于战略规划.
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