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一种基于YOLOV8和改进的DEEPLABV3的植物叶子图像分割方法
Tingting Yang1,2,3, Suyin Zhou2,3, Aijun Xu2,3
1College of Chemistry and Materials Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China.
Plants (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种结合YOLOv8和改进的DeepLabv3+的新方法,用于精确的植物叶片图像细分. 该方法显著提高了智能农林应用的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 植物科学 植物科学
背景情况:
- 精确的植物叶片细分对于诸如面积估计,物种识别和害虫监测等自动化任务至关重要.
- 现有的方法面临背景干扰和精确细分叶子结构的挑战.
研究的目的:
- 开发一种强大而精确的方法来对单个植物的叶子图像进行细分.
- 通过解决背景噪声和捕获复杂的叶子细节来改进现有的细分技术.
主要方法:
- 开发了一种混合方法,将YOLOv8用于物体检测和改进的DeepLabv3+用于细分.
- 使用YOLOv8来最大限度地减少背景干扰,随后使用了增强的DeepLabv3+,其中包括DenseASPP (Densely Connected Atrous Spatial Pyramid Pooling) 和SP (Strip Pooling) 来改进特征捕捉.
主要成果:
- 拟议的方法在公开的叶片数据集上实现了90.8%的平均交叉与结合 (mIoU).
- 它在FCN,PSPnet,U-Net和DeepLabv3变体等既定方法上表现出显著的改进,mIoU收益范围为2.5至8.4个百分点.
结论:
- YOLOv8和改进的DeepLabv3+的融合为植物叶片细分提供了卓越的性能.
- 这种先进的细分技术为智能农林和相关应用的发展提供了有效的支持.
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