• Key message

We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark.

• Context

Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality.

• Aims

Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface.

• Methods

First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator.

• Results

The defects were detected and classified with a high accuracy with an average \({F}_{1}\) score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects.

• Conclusion

The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods.

中文翻译：

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使用高密度3D地面LiDAR数据表征树干表面木材缺陷的创新自动方法

• 关键信息

我们设计了一种新颖的方法，该方法可以仅使用高密度3D信息就可以自动检测和测量来自地面LiDAR数据的树干表面的缺陷，包括分支，分支疤痕和表皮。我们可以在显示粗糙或光滑树皮的橡树（Quercus petraea（Matt。）Liebl。）或山毛榉（Fagus sylvatica L.）树上自动检测和测量直径仅为0.5 cm的缺陷。

• 语境

地面光检测和测距（LiDAR）技术以高度的细节描述了站立的树木。这提供了评估立木质量并在森林清单中使用此信息的机会。假设可以提供非常详细的信息，我们可以提取有关表面缺陷的信息，这些信息主要是从过去的分支继承而来的，并且对木材质量有很大的影响。

•目的

在开发一种能够检测，识别和量化树干表面缺陷的计算方法的总体框架内，该缺陷是由陆地LiDAR产生的3D数据描述的，该研究的重点是两个树种与树种的整个过程的相关性。在检测，缺陷识别以及与在树皮表面上手动进行的测量方面进行比较，对比了树皮的粗糙度（栎属（马格里斯）Liebl。和Fagus sylvatica L.）。

• 方法

首先，分割算法检测到躯干表面上的奇异点。接下来，Random Forests机器学习算法确定了最可能的缺陷类型，并消除了错误检测。最后，我们根据3D数据估算了每个缺陷的位置，水平和垂直尺寸，并将它们与操作员直接在行李箱上观察到的尺寸进行了比较。

• 结果

对缺陷进行了检测，并以0.74的平均\（{F} _ {1} \）分数（精度和召回率的谐和平均值）进行了高精度分类。在计算出的和观察到的缺陷区域上存在差异，但是在缺陷数量上却有着更为紧密的一致性。

• 结论

根据地面LiDAR数据测得的关于树干表面上存在的缺陷的信息，可以用于对立木或圆木进行分级的自动化过程中。