欧亚国际

稠密树叶的像素级实例分割

来自法国农业科学院等研究机构的专家，利用Hiphen公司开发的Airphen多光谱成像系统（集成与表型车上），发表了题为“Pixelwise instance segmentation of leaves in dense foliage”的文章，文章发表于Computers and Electronics in Agriculture，Volume195,April 2022,106797。

热点

叶片的像素级实例分割是在植物种类的混合体上执行的。

提出了一种新的基于形状的损耗函数，并将其应用于每个连接部件。

分割输出使用分水岭和深度索引方法进行细化。

该方法的基准测试是在“叶片分割挑战”上进行的。

共享了一个新的多光谱数据集，其中包括在自然光下采集的300幅图像。

摘要

使用图像分析检测和识别植物是精准农业（从表型到特定地点杂草管理）中许多应用的关键步骤。实例分割通常用于检测整个植物。然而，被检测物体的形状在个体和生长阶段之间会发生变化。减少这些变化的一个相关方法是缩小对叶片的检测范围。然而，当图像包含混合植物种类时，当个体重叠时，尤其是在不受控制的室外环境中，分割叶片是一项困难的任务。为了充分解决这个问题，本研究基于最近的卷积神经网络机制，提出了一种基于像素的实例分割方法来检测密集树叶环境中的树叶。它结合了“深度轮廓感知”（从其边缘的内部分离大叶子）、“叶子分割槽、边缘的分类”（以特定的内边缘分离实例）和“密集叶的金字塔CNN”（考虑不同尺度的边缘）。但分割输出也会使用分水岭和计算优化植被指数（deepindex）的方法进行优化。该方法与其他运行叶片分割挑战（由PPPN国际植物表型网络提供）的方法进行了比较，并应用于小松属植物的外部数据集。此外，还介绍了一个新的多光谱数据集，包含300幅豆类植物图像（具有浓密的叶子、个体重叠、物种混合和自然光照条件）。地面真值（例如树叶边界）由标记的多边形定义，可用于培训和评估各种专门用于树叶检测或作物/杂草分类的算法的性能。在通常的数据集上，该方法的性能与通常的叶片分割方法相似。在新的数据集上，它们的结果明显优于通常的RCNN方法。剩余的误差是相邻区域之间的融合不良和多叶叶片的过度分割造成。为了克服这些不足，可以研究结构分析方法。

关键词：精准农业、遥感、叶片分割、浓密叶片、边界检测，语义分割，CNN，多光谱

Pixelwise instance segmentation of leaves in dense foliage

Highlights

Pixelwise Instance segmentation of leaves is performed on mixes of plant species.

A new shape-based loss function is proposed and applied to each connected component.

The segmentation output is refined using a Watershed and a DeepIndices approach.

A benchmark of the method is performed on “leaf segmentation challenges”.

A new multi-spectral dataset of 300 images acquired in natural light is shared. 

Abstract

Detecting and identifying plants using image analysis is a key step for many applications in precision agriculture (from phenotyping to site specific weed management). Instance segmentation is usually carried on to detect entire plants. However, the shape of the detected objects changes between individuals and growth stages. A relevant approach to reduce these variations is to narrow the detection on the leaf. Nevertheless, segmenting leaves is a difficult task, when images contain mixes of plant species, and when individuals overlap, particularly in an uncontrolled outdoor environment. To leverage this issue, this study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment. It combines “deep contour aware” (to separate the inner of big leaves from its edges), “Leaf Segmentation trough classification of edges” (to separate instances with a specific inner edges) and “Pyramid CNN for Dense Leaves” (to consider edges at different scales). But the segmentation output is also refined using a Watershed and a method to compute optimized vegetation indices (DeepIndices). The method is compared to others running the leaf segmentation challenge (provided by the International Network on Plant Phenotyping) and applied on an external dataset of Komatsuna plants. In addition, a new multispectral dataset of 300 images of bean plants is introduced (with dense foliage, individuals overlapping, mixes of species and natural lighting conditions). The ground truth (e.g. the leaves boundaries) is defined by labelled polygons and can be used to train and assess the performance of various algorithms dedicated to leaf detection or crop/weed classification. On the usual datasets, the performances of the proposed method are similar to those of the usual methods involved in the leaf segmentation challenges. On the new dataset, their results are strongly better than those of the usual RCNN method. Remaining errors are bad fusion between neighboring areas and over segmentation of multi-foliate leaves. Structural analysis methods could be studied in order to overcome these deficiencies. 

Keywords：Precision agriculture,Remote sensing,Leaf segmentation,Dense foliage

Boundary detection,Semantic segmentation,CNN,Multispectral