ABSTRACT Macroalgae are a fundamental component of coastal ecosystems and play a key role in shaping community structure and functioning. Macroalgae are currently threatened by diverse stressors, particularly climate change and invasive species, but they do not all respond in the same way to the stressors. Effective methods of collecting qualitative and quantitative information are essential to enable better, more efficient management of macroalgae. Acquisition of high-resolution images, in which macroalgae can be distinguished on the basis of their texture and colour, and the automated processing of these images are thus essential. Although ground images are useful, labelling is tedious. This study focuses on the semantic segmentation of five macroalgal species in high-resolution ground images taken in 0.5 × 0.5 m quadrats placed along an intertidal rocky shore at low tide. The target species, Bifurcaria bifurcata, Cystoseira tamariscifolia, Sargassum muticum, Sacchoriza polyschides and Codium spp., which predominate on intertidal shores, belong to different morpho-functional groups. An explanation of how to convert vector-labelled data to raster-labelled data for adaptation to Convolutional Neural Network (CNN) input is provided. Three CNNs (MobileNetV2, Resnet18, Xception) were compared, and ResNet18 yielded the highest accuracy (91.9%). The macroalgae were correctly segmented, and the main confusion occurred at the borders between different macroalgal species, a problem derived from labelling errors. In addition, the interior and exterior of the quadrats were correctly delimited by the CNNs. The results were obtained from only one hundred labelled images and the method can be performed on personal computers, without the need to use external servers. The proposed method helps automation of the labelling process.

中文翻译：

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使用高分辨率地面图像和深度学习对沿海环境中的主要大型藻类进行语义分割

摘要 大型藻类是沿海生态系统的基本组成部分，在塑造群落结构和功能方面发挥着关键作用。大型藻类目前受到各种压力源的威胁，特别是气候变化和入侵物种，但它们对压力源的反应并不完全相同。收集定性和定量信息的有效方法对于更好、更有效地管理大型藻类至关重要。获取高分辨率图像，其中可以根据纹理和颜色区分大型藻类，因此对这些图像进行自动化处理至关重要。尽管地面图像很有用，但标记很乏味。本研究侧重于以 0.5 × 0 拍摄的高分辨率地面图像中五种大型藻类的语义分割。在退潮时沿着潮间带岩石海岸放置 5 m 样方。目标物种 Bifurcaria bifurcata、Cystoseira tamariscifolia、Sargassum muticum、Sacchoriza polyschides 和 Codium spp.，主要分布在潮间带海岸，属于不同的形态功能群。提供了如何将矢量标记数据转换为栅格标记数据以适应卷积神经网络 (CNN) 输入的说明。比较了三个 CNN（MobileNetV2、Resnet18、Xception），ResNet18 的准确率最高（91.9%）。大型藻类被正确分割，主要的混淆发生在不同大型藻类物种之间的边界处，这是由标记错误引起的问题。此外，样方的内部和外部均由 CNN 正确划定。结果仅从一百张标记的图像中获得，该方法可以在个人计算机上执行，无需使用外部服务器。所提出的方法有助于标记过程的自动化。