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一种基于激光模块和深度学习的茎直径自动测量方法。

An automated method for stem diameter measurement based on laser module and deep learning.

作者信息

Wang Sheng, Li Rao, Li Huan, Ma Xiaowen, Ji Qiang, Xu Fu, Fu Hongping

机构信息

School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.

Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, 100083, China.

出版信息

Plant Methods. 2023 Jul 5;19(1):68. doi: 10.1186/s13007-023-01045-7.

DOI:10.1186/s13007-023-01045-7
PMID:37408076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10324111/
Abstract

BACKGROUND

Measuring stem diameter (SD) is a crucial foundation for forest resource management, but current methods require expert personnel and are time-consuming and costly. In this study, we proposed a novel device and method for automatic SD measurement using an image sensor and a laser module. Firstly, the laser module generated a spot on the tree stem that could be used as reference information for measuring SD. Secondly, an end-to-end model was performed to identify the trunk contour in the panchromatic image from the image sensor. Finally, SD was calculated from the linear relationship between the trunk contour and the spot diameter in pixels.

RESULTS

We conducted SD measurements in three natural scenarios with different land cover types: transitional woodland/shrub, mixed forest, and green urban area. The SD values varied from 2.00 cm to 89.00 cm across these scenarios. Compared with the field tape measurements, the SD data measured by our method showed high consistency in different natural scenarios. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Our integrated device is low cost, portable, and without the assistance of a tripod. Compared to most studies, our method demonstrated better versatility and exhibited higher performance.

CONCLUSION

Our method achieved the automatic, efficient and accurate measurement of SD in natural scenarios. In the future, the device will be further explored to be integrated into autonomous mobile robots for more scenarios.

摘要

背景

测量树干直径（SD）是森林资源管理的关键基础，但目前的方法需要专业人员，且耗时且成本高。在本研究中，我们提出了一种使用图像传感器和激光模块自动测量SD的新型设备和方法。首先，激光模块在树干上产生一个光斑，可作为测量SD的参考信息。其次，执行一个端到端模型来识别来自图像传感器的全色图像中的树干轮廓。最后，根据树干轮廓与像素光斑直径之间的线性关系计算SD。

结果

我们在三种不同土地覆盖类型的自然场景中进行了SD测量：过渡林地/灌木丛、混交林和绿色市区。在这些场景中，SD值从2.00厘米到89.00厘米不等。与实地卷尺测量相比，我们的方法测量的SD数据在不同自然场景中显示出高度一致性。绝对平均误差为0.36厘米，均方根误差为0.45厘米。我们的集成设备成本低、便携，无需三脚架辅助。与大多数研究相比，我们的方法展示了更好的通用性并表现出更高的性能。

结论

我们的方法实现了在自然场景中对SD的自动、高效和准确测量。未来，将进一步探索该设备，以便集成到自主移动机器人中用于更多场景。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/1ea63ed13e6f/13007_2023_1045_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/f72220861da3/13007_2023_1045_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/6eda59537b58/13007_2023_1045_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/cdf991cab4e9/13007_2023_1045_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/9953056b1c6a/13007_2023_1045_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ce1/10324111/22f6df946f6d/13007_2023_1045_Fig13_HTML.jpg

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A handheld device for measuring the diameter at breast height of individual trees using laser ranging and deep-learning based image recognition.一种利用激光测距和基于深度学习的图像识别来测量单株树木胸径的手持设备。
Plant Methods. 2021 Jun 25;17(1):67. doi: 10.1186/s13007-021-00748-z.
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An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions.
一种用于编码树、测量树径和估计树位置的集成方法。
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Environ Monit Assess. 2017 Aug;189(8):416. doi: 10.1007/s10661-017-6109-x. Epub 2017 Jul 26.
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