使用YOLO-DGS在自然环境中智能检测番茄成熟
Mengyuan Zhao1, Beibei Cui2, Yuehao Yu2
1School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究介绍了YOLO-DGS,这是检测番茄成熟度的高效算法,提高了自动收获的准确性和速度. 该模型增强了特征提取,并减少了参数,以便在自然环境中获得更好的性能.
科学领域:
- 计算机视觉 计算机视觉
- 农业机器人农业机器人
- 机器学习 机器学习
背景情况:
- 自动收获需要在自然环境中准确检测番茄果实成熟度.
- 现有的算法面临着微妙的成熟度差异和果实阻塞的挑战.
研究的目的:
- 为番茄开发一种轻量级和高效的成熟度检测算法 (YOLO-DGS).
- 为了增强特征提取,降低计算成本,提高检测准确度.
主要方法:
- 提出了一种新的分段智能卷积模块 (C2f-GB),用于在降低参数的情况下增强特征提取.
- 通过删除冗余层并集成双向特征金字塔网络 (BiFPN) 来修改YOLO v10.
- 引入了一个频道注意力机制,用于动态信息利用.
主要成果:
- YOLO-DGS在F1分数上升了2.6%,在回忆中提高了2.1%,并改善了mAP分数.
- 推断速度增加了12.5%,参数减少了26.3%.
- 在性能和效率方面表现优于主流的轻量级物体检测模型.
结论:
- 对于番茄成熟度检测,YOLO-DGS提供了一种高效准确的解决方案.
- 该算法适用于在自然环境中运行的自动化番茄收获机器人的视觉系统.
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