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Scientia Silvae Sinicae ›› 2025, Vol. 61 ›› Issue (4): 20-32.doi: 10.11707/j.1001-7488.LYKX20240617

• Special subject: Smart forestry • Previous Articles    

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

Chang Le1, Du Xiaochen1,2, Feng Hailin1,2,3, Li Yan’e1, Huang Jianqin3   

  1. 1. College of Mathematics and Computer Science, Zhejiang A & F University Hangzhou 311300;
    2. Key Laboratory of Forestry Perception Technology and Intelligent Equipment, National Forestry and Grassland Administration Hangzhou 311300;
    3. State Key Laboratory of Subtropical Silviculture Hangzhou 311300
  • Received:2024-10-22 Revised:2025-01-19 Published:2025-04-21

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

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