Open Access

ARTICLE

Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

by Wei Song^1,2,3, Haolin Wang¹, Xinping Zhang¹, Jianxin Xia^4,*, Tongmu Liu⁵, Yuxi Shi⁶

1 School of Information and Engineering, Minzu University of China, Beijing, 100081, China
2 Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resource, Guangzhou, 510300, China
3 National Language Resource Monitoring & Research Center of Minority Languages, Beijing, 100081, China
4 School of Ocean Science, China University of Geosciences, Beijing, 100191, China
5 Department of Buoy Engineering, South China Sea Marine Survey and Technology Center, Guangzhou, 510300, China
6 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, 07102, USA

* Corresponding Author: Jianxin Xia. Email:

Computers, Materials & Continua 2022, 73(1), 1449-1462. https://doi.org/10.32604/cmc.2022.027213

Received 12 January 2022; Accepted 10 March 2022; Issue published 18 May 2022

Abstract

Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation. The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining. The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer. In order to improve the segmentation performance of underwater mineral images, this paper uses the Pix2PixHD (Pixel to Pixel High Definition) algorithm based on Conditional Generative Adversarial Network (CGAN) to segment deep-sea mineral images. The model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks, and multiple multi-scale discriminators with same network structures but different input pictures to generate high-quality images. The test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other conditions. The evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net algorithm. It is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.

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Keywords

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