DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery

The detection and preservation of marine biodiversity has garnered global attention. The incorporation of deep learning methodologies can elevate the efficiency of species detection. In this study, we developed a DeepSeaNet for effective localization and accurate classification of organisms based on deep-sea images, as well as for hinting at unknown organisms (new species). The DeepSeaNet fully accommodates the unique characteristics of deep-sea organisms and imaging environment, leading to remarkable advancements in fine-grained analysis and accuracy. The DeepSeaNet comprises two network components: a deep-sea Classes Detection Network (CDN) and an unsupervised Species Clustering Network (SCN). CDN is used for biological class detection and is specifically tailored for deep-sea environments. It incorporates modules for feature fusion, multi-scale analysis, and self-attention. SCN is specifically designed to detect and identify new species by utilizing the location information extracted from the CDN output results. It is composed of a feature extraction module and a clustering module. By collecting deep-sea image data from the “KeXue” Science Research Vessel, we constructed a dataset totaling 29,436 images of deep-sea organisms covering more than 500 species of deep-sea seamount organisms. This dataset serves as the foundational dataset for our experiment. As a result, our model achieves an 82.18% mean average precision for class detection and a 43.4% accuracy for species detection. Furthermore, the model has the capability to identify new species through the computation of inter-species distances.