一种基于GC-U-Net模型的立木胸径自动测量方法

doi:10.11707/j.1001-7488.LYKX20240617

Abstract

Abstract:

Objective: This study aims to address the problems of time-consuming and labor-intensive, susceptible to human error, and expensive equipment in the traditional process of measuring the diameter at breast height (DBH) of standing trees, for which an automatic method for measuring DBH of standing trees based on smartphone and deep learning is proposed. Method: First, in order to meet the demand for low-cost automatic measurement of standing tree DBH, a smartphone was used to acquire monocular RGB images of standing trees. Then a standing tree image segmentation algorithm based on GC-U-Net model was proposed to accurately extract the contours of standing trees. Based on the traditional U-Net segmentation model, the model's ability to recognize the features of the trunk of a standing tree was enhanced by integrating the VGG16 and CBAM attention mechanisms. Finally, based on photogrammetric principles, a standing tree diameter measurement model was constructed, and used to quickly and accurately calculate DBH of the standing trees using the segmented image. Result: The comparative experimental results showed that the GC-U-Net model improved the mean intersection ratio (mIoU) by 4.38%, the mean pixel accuracy (mPA) by 6.08%, and the recall by 4.85% over the traditional U-Net model. Compared with the PSPNet, SegNet, and Deeplabv3+ segmentation models, the GC-U-Net model also obtained better trunk segmentation, with mIoU being improved by 6.04%, 6.52%, and 11.0%, mPA being improved by 7.93%, 7.36%, and 12.31%, and Recall being improved by 6.88%, 7.83%, and 11.08%, respectively. The average relative error of trunk diameter measurement was only 2.37%, and the goodness-of-fit reached 0.91. Conclusion: Compared with the traditional method of measuring DBH, the method in this study can automatically obtain the DBH value of standing trees by only acquiring a monocular image of standing trees from a smartphone, which reduces the cost of measurement equipment. The image acquisition process does not require a reference, which simplifies the complexity of on-site measurements and improves the efficiency of the measurements. At the same time, the proposed GC-U-Net model ensures the measurement accuracy of the standing tree DBH.

Key words: GC-U-Net, trunk segmentation, measurement of diameter at breast height, mobile phone

CLC Number:

S758

Le Chang,Xiaochen Du,Hailin Feng,Yan’e Li,Jianqin Huang. An Automatic Measurement of Standing Tree Diameter at Breast Height Based on the GC-U-Net Model[J]. Scientia Silvae Sinicae, 2025, 61(4): 20-32.

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配置Configuration	参数Parameter
中央处理器Central processing unit (CPU) 图形处理器Graphics processing unit (GPU) 开发环境Development environment 加速环境Accelerated environment 模型优化器Model optimizer 训练轮数Epochs 学习率Learning rate	Intel(R) Core (TM) i5-13490F NVIDIA GeForce RTX4060Ti Pycharm CUDA12.1 CUDNN8.2.1 Adam 200 0.01

模型Model	mIoU（%）	mPA（%）	Recall（%）
DeepLabv3+	80.04	81.59	81.41
PSPNet	79.56	82.16	80.46
SegNet	75.09	77.21	77.21
U-Net	81.7	83.44	83.44
Swin-UNet	73.9	76.12	76.12
GC-U-Net	86.08	89.52	88.29

模型改进Model improvement	mIoU（%）	mPA（%）	$ \text{Rec}\text{all} $（%）
Axial	83.15	86.38	86.38
SE	82.92	85.79	85.21
CBAM	85.19	89.52	88.29
Axial+SE	82.06	84.61	84.61
Axial+CBAM	80.97	83.46	83.46
SE+CBAM	84.55	86.85	86.85
Axial+SE+CBAM	81.34	84.86	83.44
SA	84.52	87.18	87.18
PPM	84.89	87.62	87.62

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An Automatic Measurement of Standing Tree Diameter at Breast Height Based on the GC-U-Net Model

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