Semantic Segmentation of seafloor images in Philippines based on semi-supervised learning
Semantic Segmentation of seafloor images in Philippines based on semi-supervised learning
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DOI：
10.1109/ut49729.2023.10103432
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Shulei Wang;K. Mizuno;S. Tabeta;Terayama Kei
中科院分区：
文献类型：
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作者：
Shulei Wang;K. Mizuno;S. Tabeta;Terayama Kei
海洋图像的语义分割可以用于海底场景的描述和海洋生物的监测。然而，为图像分割准备人工标注数据集是一项耗时的任务。因此，本文提出了一种基于Mean-Teacher和U-Net模型相结合的半监督语义分割算法，对菲律宾海底图像进行分类。该方法将对图像的两个部分进行训练和验证。一方面，对于包含珊瑚、海胆、海星等类别（包括沉积物和海草）的图像，使用普通标记进行训练和验证。另一方面，对于仅包含海草和沉积物类别的图像，人工标记海草类别特别困难。为了克服这一障碍，根据这类图像的特点，采用K-means聚类算法获得标记数据集进行训练和验证。与基于U-Net的监督方法相比，本文提出的半监督方法在标记图像较少的情况下也取得了较好的效果和精度值。
Semantic segmentation of marine images can be used to describe seafloor scenes and monitor marine creatures. However, preparing human-annotated datasets for image segmentation is time-consuming task. Therefore, this paper proposes a semi-supervised semantic segmentation algorithm based on the combination of Mean-Teacher and U-Net models to classify seafloor images collected in Philippines. The method will train and validate on two parts of the image. On the one hand, for images containing categories of coral, sea urchin, sea stars, and others (including sediment and seagrass), ordinary labeling is used for training and validation. On the other hand, for images only including seagrass and sediment categories, manual labeling of seagrass categories is particularly difficult. In order to overcome this barrier, based on the characteristics of this type of images, K-means clustering algorithm is used to obtain labeled dataset for training and validation. Compared with the U-Net based supervised method, the semi-supervised method proposed in this paper achieves good results and accuracy values even with fewer labeled images.