Abstract
Autonomous underwater vehicle (AUV) has many intelligent optical system, which can collect underwater signal information to make the system decision. One of them is the intelligent vision system, and it can capture the images to analyze. The performance of the particle image segmentation plays an important role in the monitoring of underwater mineral resources. In order to improve the underwater mineral image segmentation performance, some novel segmentation algorithm architectures are proposed. In this paper, an improved mineral image segmentation is proposed based on the modified U-Net. The pyramid upsampling module and residual module are bring into the U-Net model, which are called JPU-Net, JPMU-Net and ResU-Net. These models combined the power of the residual block and the pyramid upsampling in the encoder part and in the decoder part respectively. The proposed models are tested on the Electron Microscopy images (EM) dataset and the underwater mineral image dataset. The experimental results show that JPU-Net has superior performance on the EM dataset, and JPMU-Net has a better segmentation result than existing convolutional neural network on the underwater mineral image dataset.
Highlights
The ocean occupies 70% of the earth’s total area and is rich in mineral resources
IASC, 2022, vol.32, no.3 nodule mines in deep-sea mining areas due to insufficient sampling samples, and the mining area resource evaluation accuracy is relatively low; the deep tow system is equipped with an underwater vision optical device, which uses Non-contact photographic detection makes continuous visual sampling of minerals in the mining area possible without destroying the seabed environment, and obtains a large number of rich images of deep-sea minerals
In order to evaluate the performance of the model, verify it on a challenging benchmark: the gray-scale Electron Microscopy images (EM dataset), and apply it to the underwater mineral image dataset to find the relatively suitable segmentation model
Summary
The ocean occupies 70% of the earth’s total area and is rich in mineral resources. The exploration and effective mining of solid ore resources such as polymetallic sulfides, polymetallic nodules and cobalt nodules in the deep-sea can effectively alleviate the current shortage of land resources. Variants of the encoder-decoder architecture like U-Net [16] and fully-connected convolutional neural network (FCN) [17] provide state-of-the-art results for image segmentation tasks in computer vision. Another variant of FCN was proposed which is called SegNet [18]. A hierarchical probability U-Net segmentation network [23] was proposed, this method is based on conditional variational autoencoder (cVAE) to high fidelity sample and reconstructs segments This structure provides the flexibility of learning the distribution of cross-scale complex structures and has a good performance for fuzzy medical image segmentation. Migrated the improved model to the underwater mineral image dataset, and the experimental comparison yielded the best performing model for the dataset
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