Graph Refinement in Latent Space: A Hypergraph Convolution for Underwater Object Detection

Underwater object detection presents significant challenges due to the intrinsic properties of light in aquatic environments. State-of-the-art methods often fail to capture the subtle details necessary for accurate detection in these scenarios. Recent advancements have shown promising results by reformulating relationships in graph space; however, most SOTA methods typically employ graph structures that are insufficient to represent the complex latent variables inherent in underwater environments. Hence, these models are unable to preserve the actual boundaries of object detection. To address these limitations, in this paper, we propose a novel end-to-end architecture that uses a graph refactoring aware deep learning based encoder-decoder architecture. The proposed approach uses a convolutional backbone to project the image into latent space, where an unsupervised initial graph is constructed. The hypergraph convolution is then utilized to optimize message passing between graph nodes, enhancing the representation of complex relationships of latent space. This helps in the retention of intricate details by modelling two or more latent variables as hyperedge by sharing the information among themselves. Finally, an image generation module maps the enhanced graph representation back to image space. The effectiveness of the proposed method is demonstrated through a comparative analysis against fourteen state-of-the-art methods on the benchmark underwater databases. The code for the proposed scheme can be found at https://github.com/immkapoor/hyper_graph.