SVGS-DSGAT: An IoT-enabled innovation in underwater robotic object detection technology

Abstract

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 incorporates GraphSage for capturing and processing complex structural data, SVAM for guiding attention toward critical features, and DSGAT for refining feature relationships by emphasizing differences and similarities. These components work together to enhance the model’s robustness and precision in underwater target recognition and tracking. 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.

Keywords

• Underwater object detection
• Internet of Things
• SVGS-DSGAT model
• SVAM model
• Deep learning