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

摘要/Abstract

摘要： 目的针对传统测量立木胸径过程中费时耗力、易受人为因素影响、设备价格昂贵等问题，提出一种基于智能手机和深度学习的立木胸径自动测量方法。方法首先，为达到低成本的立木胸径自动测量需求，采用智能手机获取立木单目RGB图像；然后，为精准提取立木轮廓，提出一种基于GC-U-Net模型的立木图像分割算法，在传统U-Net分割模型基础上，集成VGG16和CBAM增强模型对立木树干特征的识别能力；最后，基于摄影测量原理，构建立木胸径测量模型，利用分割后的树干图像快速准确地计算出立木胸径。结果相较传统U-Net模型，GC-U-Net模型的平均交并比（mIoU）提升4.38%，平均像素准确率（mPA）提升6.08%，召回率（Recall）提升4.85%；相比Deeplabv3+、PSPNet、SegNet分割模型，GC-U-Net模型可取得更好的树干分割结果，mIoU分别提升6.04%、6.52%、11.0%，mPA分别提升7.93%、7.36%、12.31%，Recall分别提升6.88%、7.83%、11.08%；树干直径测量平均相对误差仅2.37%，拟合度达0.91。结论与传统立木胸径测量方法相比，本研究方法仅通过智能手机获取立木单目图像即可自动获取胸径，全程无需昂贵的专业设备；图像采集过程中也不需要参照物，简化了现场测量的复杂性，提高了测量效率；同时，GC-U-Net模型还可确保立木胸径测量的准确度。

关键词: GC-U-Net, 树干分割, 胸径测量, 智能手机

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

中图分类号:

S758

常乐, 杜晓晨, 冯海林, 李颜娥, 黄坚钦. 一种基于GC-U-Net模型的立木胸径自动测量方法[J]. 林业科学, 2025, 61(4): 20-32.

Chang Le, Du Xiaochen, Feng Hailin, Li Yan’e, Huang Jianqin. 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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