Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

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 highquality 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.

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

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.