Semi-supervised learning network for deep-sea nodule mineral image segmentation
Year: 2024
Type: article
Abstract: The accurate segmentation of deep-sea nodule mineral images is crucial for scientific mining. However, due to the low contrast of deep-sea images and the varying sizes of nodule minerals, existing methods are not effective in segmenting these images. Furthermore, fully supervised deep learning methods require numerous labelled images for training, and labelling deep-sea nodule mineral images is highly difficult, resulting in a scarcity of available labeled images, which limits the model generalization ability. To address these challenges, a semi-supervised learning network for deep-sea nodule image segmentation (NmiNet) was proposed. In this method, a semi-supervised training paradigm based on underwater image enhancement perturbation and uncertainty weighted optimization (UEUO) was designed. This paradigm enabled the model to fully mine the features in many unlabeled nodule mineral images under the condition of few labelled nodule mineral images, improving the model generalization ability. Moreover, a lightweight global and local feature extraction (GLFE) module was designed to enhance the attention of the module to small nodules, and its ability to locate nodules of different scales by fusing local and global features without considerably increasing model complexity. Experimental results on deep-sea nodule mineral images reveal that this method outperforms existing approaches.
Source: Applied Ocean Research
Authors: Zhongjun Ding, Chen Liu, Xingyu Wang, Guangyang Ma, Chanjuan Cao, Dewei Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: hybrid
Estimated APC paid: $4,080
OpenAlex ID: W4405006320