一个基于改进的YOLOv7的树种分类模型,用于庇护带
Yihao Liu1,2, Qingzhan Zhao1,2, Xuewen Wang3
1College of Information Science and Technology, Shihezi University, Shihezi, China.
Frontiers in plant science
|February 2, 2024
概括
一个新的YOLOv7-Kmeans++_CoordConv_CBAM (YOLOv7-KCC) 模型有效地使用无人机图像将树种分类为庇护带. 这种先进的模型显著提高了准确性,以改善像新疆这样的地区的森林管理.
科学领域:
- 林业科学 林业科学
- 遥感技术 遥感技术 遥感技术
- 生态学中的人工智能
背景情况:
- 庇护带管理需要准确的树种分类,这是当前基于卫星和无人机的方法的挑战,因为复杂的背景和相似的树冠大小.
- 现有的方法在混合生长保护森林中难以区分单个树种,影响管理策略.
- 你只看一次 (YOLO) 算法在林业应用中显示出希望,包括树种识别.
研究的目的:
- 开发和评估一种新的深度学习模型,用于使用无人机RGB图像在庇护带中精确地分类树种.
- 增强YOLOv7架构,以改善复杂森林环境中的特征提取和融合.
- 为保护区管理,特别是中国西北地区的科学理论基础提供一个强大的工具.
主要方法:
- 创建了庇护带树种的专用数据集,将数据增强纳入数据增强,以解决有限的训练数据.
- 用K-means++算法进行了最佳的框集群.
- 在ELAN模块中修改了YOLOv7骨干网络,在ELAN模块中使用了坐标卷积 (CoordConv),并将卷积区注意模块 (CBAM) 集成到PANet中,以增强特征表示.
主要成果:
- 拟议的YOLOv7-Kmeans++_CoordConv_CBAM (YOLOv7-KCC) 模型实现了98.91%的平均平均精度@0.5.
- YOLOv7-KCC显著超过了包括Faster RCNN (VGG16,Resnet50),SSD,YOLOv4和基础YOLOv7模型在内的已有的模型.
- 该模型显示F1指标与YOLOv7相比增加了5.6%,计算成本为105.07GFlops和143.7MB参数.
结论:
- YOLOv7-KCC模型提供了一种高效的解决方案,用于在无人机图像的庇护带内对树种进行分类.
- 这一进步为优化庇护带管理实践提供了至关重要的科学基础,特别是在干旱和半干旱地区.
- 整合CoordConv和CBAM模块增强了该模型在林业应用中处理复杂视觉数据的能力.
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