TY  - EJOU
AU  - Song, Wei 
AU  - Dong, Lihui 
AU  - Zhao, Xiaobing 
AU  - Xia, Jianxin 
AU  - Liu, Tongmu 
AU  - Shi, Yuxi 

TI  - Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 73
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Polymetallic nodule; deep-sea mining; image segmentation; deep learning

DO  - 10.32604/cmc.2022.027214