Open Access

ARTICLE

Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment

Wei Song^1,2,3, Lihui Dong¹, Xiaobing Zhao^1,3, 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, Minzu University of China, 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 of State Oceanic Administration, Guangzhou, 510310, China
6 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

* Corresponding Author: Jianxin Xia. Email:

Computers, Materials & Continua 2022, 73(1), 1649-1669. https://doi.org/10.32604/cmc.2022.027214

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

Abstract

A large number of nodule minerals exist in the deep sea. Based on the factors of difficulty in shooting, high economic cost and high accuracy of resource assessment, large-scale planned commercial mining has not yet been conducted. Only experimental mining has been carried out in areas with high mineral density and obvious benefits after mineral resource assessment. As an efficient method for deep-sea mineral resource assessment, the deep towing system is equipped with a visual system for mineral resource analysis using collected images and videos, which has become a key component of resource assessment. Therefore, high accuracy in deep-sea mineral image segmentation is the primary goal of the segmentation algorithm. In this paper, the existing deep-sea nodule mineral image segmentation algorithms are studied in depth and divided into traditional and deep learning-based segmentation methods, and the advantages and disadvantages of each are compared and summarized. The deep learning methods show great advantages in deep-sea mineral image segmentation, and there is a great improvement in segmentation accuracy and efficiency compared with the traditional methods. Then, the mineral image dataset and segmentation evaluation metrics are listed. Finally, possible future research topics and improvement measures are discussed for the reference of other researchers.

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Keywords

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