峰检测方法基于金色特征金字塔模块和改进的YOLOv8s
Shujin Qiu1,2, Jian Gao1,2, Mengyao Han1,2
1College of Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了一种改进的YOLOv8s模型,用于准确检测麦尖,解决诸如高密度和遮等挑战. 改进后的模型显著提高了现场条件下的精度和回忆力.
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:
- 由于种植密度高,颜色相似和遮蔽,发现棘棘是具有挑战性的.
- 现有方法的准确性低,错误检测率高,检测率错误.
研究的目的:
- 开发一种改进的棘检测方法,以克服当前的局限性.
- 为了提高在自然田间环境中识别棘的准确性和可靠性.
主要方法:
- 一个改进的YOLOv8s模型 (YOLOv8s-Gold-LSKA) 通过集成金色特征金字塔模块和通过LSKA注意力机制完善SPPF模块来开发.
- 使用焦点-EIOU损失函数来解决类不平衡并加快模型融合.
主要成果:
- YOLOv8s-Gold-LSKA模型的精度达到了90.72%,回忆率达到了76.81%,mAP达到了85.86%,F1得分达到了81.19%.
- 与YOLOv5s,SSD和YOLOv8.8相比,改进的模型显示出更高的检测性能.
结论:
- 拟议的方法显著提高了在自然野外环境中检测棘的准确性.
- 这一进步为准确的果产量估计和智能收获设备提供了技术支持.
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