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.

This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. Lower-resolution features are more sensitive to larger objects closer to the camera, while higher-resolution features are more efficient for smaller objects further away. Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. Motivated by this, we propose a multi-resolution scale feature fusion-based universal density counting network (MRSNet). It utilizes independent modules to process high- and low-resolution features, adaptively adjusts receptive field sizes, and incorporates dynamic sparse attention mechanisms to optimize feature information at each resolution, by integrating optimal features across multiple scales into density maps for counting evaluation. Our proposed network effectively mitigates issues caused by large variations in object scales, thereby enhancing counting accuracy. Furthermore, extensive quantitative analyses on six public datasets demonstrate the algorithm’s strong generalization ability in handling diverse object scale variations.