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总理YOLO:一个粉状菌自动分级检测模型树的树
Yuheng Li1,2, Qian Chen1,2, Jiazheng Zhu3,4
1School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China.
Insects
|January 8, 2025
概括
一个新的深度学习模型,PM-YOLO,准确地检测树上的粉状菌. 这种自动分级系统为早期疾病干预的传统方法提供了更快,更有效的替代方案.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 粉状菌显著影响树的产量和质量.
- 早期检测至关重要,但传统方法效率低下.
- 需要自动检测疾病,以便及时进行干预.
研究的目的:
- 开发一种深度学习模型,以准确高效地检测树中的粉状菌.
- 为训练检测模型创建一个全面的数据集.
- 实施一个自动分级算法来评估疾病的严重程度.
主要方法:
- 构建了一个由6200个树图像和38000个注释组成的数据集.
- 开发了基于YOLO框架的深度学习模型PM-YOLO.
- 集成了一个特征聚焦和扩散机制 (FFDM) 和维度意识选择性集成 (DASI) 模块.
- 提出了一种针对疾病严重程度的自动分级算法.
主要成果:
- PM-YOLO实现了86.9%的平均平均精度 (mAP) 和85.6%的回忆.
- 超过标准YOLOv10的表现为7.6%mAP和8.2%召回.
- 在复杂的背景和有效的分级中证明了准确的检测.
结论:
- 拟议的PM-YOLO模型提供了精确的实时检测树粉状真菌.
- 自动分级系统为早期诊断和管理提供了有效的解决方案.
- 这种深度学习方法解决了传统疾病检测方法的局限性.
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