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.

翻译：随着物联网技术的进步，水下目标检测与跟踪对于海洋监测和资源管理日益重要。现有方法在处理复杂水下环境中的高噪声、低对比度图像时，往往在精度和鲁棒性方面存在不足。本文提出了一种新颖的SVGS-DSGAT模型，该模型融合了GraphSage、SVAM和DSGAT模块，通过图神经网络和注意力机制增强了特征提取与目标检测能力。该模型集成物联网技术，以促进实时数据采集与处理，优化资源分配和模型响应能力。实验结果表明，SVGS-DSGAT模型在URPC 2020数据集上实现了40.8%的mAP，在SeaDronesSee数据集上实现了41.5%的mAP，显著优于现有主流模型。这种物联网增强的方法不仅在高噪声和复杂背景下表现优异，还提升了系统的整体效率与可扩展性。本研究为水下目标检测技术提供了一种有效的物联网解决方案，具有重要的实际应用价值和广阔的发展前景。