在棉花田中优化杂草识别算法的YOLOv8模型的改进
Lu Zheng1,2, Junchao Yi1, Pengcheng He1
1College of Computer Science, South-Central Minzu University, Wuhan 430074, China.
Plants (Basel, Switzerland)
|July 13, 2024
概括
一个新的YOLOv8-DMAS模型增强了复杂领域的棉花杂草检测. 这种改进的模型提供了更高的准确性和回忆力,支持智能除草研究.
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:
- 在复杂的环境中检测棉花杂草是具有挑战性的,因为不同的物种,密集的分布,封闭,和小目标.
- 现有的YOLO算法在这种情况下经常受到低准确度和错误检测的影响.
研究的目的:
- 提出一个改进的YOLOv8-DMAS模型,用于在复杂的田间条件下准确和实时检测棉花杂草.
- 增强模型在捕获多尺度特征和解决特征融合中的空间不一致性方面的能力.
主要方法:
- 修改了C2f网络,将BottleNeck模块替换为Dilation-wise剩余模块 (DWR).
- 将多尺度模块 (MSBlock) 集成到骨干中,并向头部添加了一个小目标检测层.
- 实现了自适应空间特征融合 (ASFF),以改善特征融合,并用SoftNMS取代非最大抑制 (NMS).
主要成果:
- 与YOLO v8s相比,YOLOv8-DMAS模型显示了精度 (1.7%),回忆 (3.8%),mAP0.5 (2.1%) 和mAP0.5:0.95 (3.7%) 的改善.
- 与YOLOv5s,YOLOv7和SSD相比,实现了优越的性能,在mAP0.5:0.95中分别提高了4.8%,4.5%和5.9%.
- 在复杂的环境中展示了精确的实时检测棉花杂草.
结论:
- 拟议的YOLOv8-DMAS模型有效地解决了现有的棉花杂草检测算法的局限性.
- 这些增强功能能够对各种杂草类型进行强有力的检测,包括小型和封闭的目标.
- 这项研究为智能除草系统的发展提供了至关重要的技术支持.
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