SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology

TL;DR

This paper presents SVGS-DSGAT, an IoT-enabled underwater object detection model that combines graph neural networks and attention mechanisms to improve accuracy and robustness in complex, noisy underwater environments.

Contribution

The paper introduces a novel IoT-integrated model combining GraphSage, SVAM, and DSGAT modules for enhanced underwater object detection, outperforming existing methods.

Findings

Achieved 40.8% mAP on URPC 2020 dataset.

Achieved 41.5% mAP on SeaDronesSee dataset.

Significantly outperforms existing models in noisy underwater conditions.

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 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.