基于从空中遥感图像中改进的YOLOv7自动检测立死树的自动检测
Hongwei Zhou1, Shangxin Wu1, Zihan Xu1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
Frontiers in plant science
|February 6, 2024
概括
这项研究引入了一种改进的YOLOv7模型,具有SimAM注意力和WIoU损失,用于在遥感图像中检测站着的死树 (SDTs). 改进后的模型显著提高了检测准确度,有助于森林管理.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 准确检测立死树木 (SDT) 对森林管理和保护至关重要.
- 传统的手动测量在艰难的地形上具有挑战性.
- 深度学习和遥感为高效的SDT检测提供了潜在的潜力,但在识别小,封闭或背景伪装的树木方面仍然存在挑战.
研究的目的:
- 开发一种改进的深度学习模型,用于在空中遥感图像中准确地检测SDT的单树尺度.
- 为了增强特征提取和死树检测中的小目标灵敏度.
- 提高现有的物体检测模型的稳定性和准确性,用于森林应用.
主要方法:
- 一个改进的You Only Look Once版本7 (YOLOv7) 模型被开发出来,包含了简单的无参数注意力模块 (SimAM).
- 为了增强模型的稳定性,完整的交叉与欧盟 (CIoU) 损失函数被替换为智能IoU (WIoU) 损失函数.
- 拟议的SimAM_YOLOv7模型的性能与其他四种注意力机制以及使用SDTs自主开发的数据集的原始YOLOv7模型进行了评估.
主要成果:
- SimAM_YOLOv7模型实现了高检测精度,精度,回忆和mAP@0.5值分别为94.31%,93.13%和98.03%.
- 这些性能指标代表了与原始YOLOv7模型相比的显著改进,精度增加了3.67%,回忆增长了2.28%,mAP@0.5.5.增长了1.56%.
- 改进后的模型在复杂的背景和封闭中检测小目标死树方面表现出了卓越的能力.
结论:
- 改进的YOLOv7模型与SimAM和WIoU有效地检测空中遥感图像中的死树.
- 这种方法为大规模的森林库存和管理提供了更方便,更准确的解决方案.
- 该研究强调了集成先进的注意力机制和损失功能的潜力,以改善基于遥感的生态监测.
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