Polymetallic Nodule Resource Assessment of Seabed Photography Based on Denoising Diffusion Probabilistic Models

Abstract

Polymetallic nodules, found abundantly in deep-ocean deposits, possess significant economic value and represent a valuable resource due to their high metal enrichment, crucial for the high-tech industry. However, accurately evaluating these valuable mineral resources presents challenges for traditional image segmentation methods due to issues like color distortion, uneven illumination, and the diverse distribution of nodules in seabed images. Moreover, the scarcity of annotated images further compounds these challenges, impeding resource assessment efforts. To overcome these limitations, we propose a novel two-stage diffusion-based model for nodule image segmentation, along with a linear regression model for predicting nodule abundance based on the coverage obtained through nodule segmentation. In the first stage, we leverage a diffusion model trained on predominantly unlabeled mineral images to extract multiscale semantic features. Subsequently, we introduce an efficient segmentation network designed specifically for nodule segmentation. Experimental evaluations conducted on a comprehensive seabed nodule dataset demonstrate the exceptional performance of our approach compared to other deep learning methods, particularly in addressing challenging conditions like uneven illumination and dense nodule distributions. Our proposed model not only extends the application of diffusion models but also exhibits superior performance in seabed nodule segmentation. Additionally, we establish a linear regression model that accurately predicts nodule abundance by utilizing the coverage calculated through seabed nodule image segmentation. The results highlight the model’s capacity to accurately assess nodule coverage and abundance, even in regions beyond the sampled sites, thereby providing valuable insights for seabed resource evaluation.