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Segmentation of Seabed Sediment Images Based on Convolutional Neural Network
Yanchang Liu, Ke Du, Liqun Shan, Lei Zhu, Hanyu Jiang, Yan Wang and Xiali Hei
The analysis of particle sizes in seabed sediments plays an important role in various fields. However, acquiring digital sediment images has been challenging due to the characteristics of sediment particles and the complexity of the seabed exploration environment. The goal of this study is to address the issue of particle segmentation in sediment images and propose an innovative network called SAMU-Net. Innovations are as follows. Firstly, SAMU-Net combines spatial attention modules with the traditional U-Net model, which allows for contextual information extraction and adaptive perception of feature variations. Secondly, the model adopts the idea of multi-scale fusion, enabling networks to better understand the structural and semantic information of the images by fusing feature maps at different scales. In addition, using the drop blocks and normalization, we speed up the training convergence of the model and avoid the overfitting phenomenon. The results show that the segmentation results predicted by the SAMU-Net network are highly similar to the ground truth and have a better segmentation performance compared to the existing algorithms. At the same time, it provides a new solution idea to uneven illumination and other noise interference problems in the digital imaging information of sediments.
Keywords: Sediments, Particle size, U-Net, Spatial attention modules, Image segmentation