基于改进的YOLOv7的幸运竹节点的自动检测
Jing Zhang1,2,3, Ruoling Deng1,2,3, Chengzhi Cai1
1School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Frontiers in plant science
|August 1, 2025
概括
这项研究引入了一种增强的YOLOv7模型,用于准确的幸运竹节点检测,提高手工艺生产的效率. 该模型实现了高精度和速度,提供了可行的自动化解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 手动检测幸运的竹子 (Dracaena sanderiana) 节点是低效的,容易出错.
- 对于高价值的手工艺品生产,需要自动化解决方案.
研究的目的:
- 开发一个改进的YOLOv7模型,实时精确检测幸运的竹节点.
- 为了增强对象本地化和边界框回归,以提高准确性.
主要方法:
- 集成的挤压刺激 (SE) 注意力机制进入特征提取网络.
- 实施的权重交叉与联盟 (WIoU) 损失函数用于界限框回归.
- 在各种条件下利用了2000张注释图像的数据集,并在RTX 4090 GPU上进行训练.
主要成果:
- 实现了97.6%的mAP@0.5,比原来的YOLOv7.2提高了14.2%.
- 保持了100.18 FPS的高推断速度,优于其他最先进的模型.
- 在低光和遮蔽等具有挑战性的条件下证明了强度和可靠性.
结论:
- 增强的YOLOv7模型在检测准确性和效率方面提供了显著的改进.
- 为智能农业和手工制造业的工业应用提供了一个可行的工具.
- 未来的工作将专注于改善严重堵塞或模式节点的检测.
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