Marine Debris Segmentation Using a Parameter Efficient Octonion-Based Architecture
Alabi Bojesomo, Panos Liatsis, Hasan Al-Marzouqi
Abstract
Marine debris poses a significant ecological challenge, necessitating advanced methods for its accurate detection and segmentation. Deep learning enables advanced remote sensing capabilities for earth observation, however, its on-board deployment is hindered by limitations in resource availability. In this paper, octonion neural networks (ONNs) are proposed for developing a parameter-efficient solution to address the problem of marine debris segmentation. ONNs extend the capabilities of real-valued networks by incorporating octonions, an eight-dimensional hypercomplex number system. By harnessing the power of octonions, such as their ability to capture higher-dimensional relationships and extract robust feature representations, enhanced segmentation accuracy can be achieved. The proposed ONN model is evaluated on the MARIDA dataset, a comprehensive benchmark for marine debris segmentation. The results demonstrate that the proposed approach outperforms the state of the art, achieving remarkable improvements of 9.9% and 7.6% in terms of the Intersection over Union (IoU) and F1 metrics, respectively. Moreover, the ONN approach delivers performance similar to that of the real-valued architecture, while utilizing 1/13 of the network parameters.