SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology
This provides an IoT-enhanced solution for ocean monitoring and resource management, though it appears incremental as it builds on graph neural networks and attention mechanisms.
The paper tackles the problem of underwater target detection in high-noise, low-contrast environments by introducing the SVGS-DSGAT model, which achieves mAP scores of 40.8% on URPC 2020 and 41.5% on SeaDronesSee, outperforming existing models.
With the advancement of Internet of Things (IoT) technology, underwater target detection and tracking have become increasingly important for ocean monitoring and resource management. Existing methods often fall short in handling high-noise and low-contrast images in complex underwater environments, lacking precision and robustness. This paper introduces a novel SVGS-DSGAT model that combines GraphSage, SVAM, and DSGAT modules, enhancing feature extraction and target detection capabilities through graph neural networks and attention mechanisms. The model integrates IoT technology to facilitate real-time data collection and processing, optimizing resource allocation and model responsiveness. Experimental results demonstrate that the SVGS-DSGAT model achieves an mAP of 40.8% on the URPC 2020 dataset and 41.5% on the SeaDronesSee dataset, significantly outperforming existing mainstream models. This IoT-enhanced approach not only excels in high-noise and complex backgrounds but also improves the overall efficiency and scalability of the system. This research provides an effective IoT solution for underwater target detection technology, offering significant practical application value and broad development prospects.