融合物联网与图神经网络，提升水下目标检测精度与实时性。
SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology
- 用图神经网络和注意力机制增强特征提取能力
- 在两个数据集上分别达到40.8%和41.5%的mAP
- 适合海洋监测、资源管理等实际场景应用
随着物联网（IoT）技术的发展，水下目标检测与追踪在海洋监测和资源管理中日益重要。现有方法在复杂水下环境中难以应对高噪声、低对比度图像，精度与鲁棒性不足。本文提出一种新型SVGS-DSGAT模型，融合GraphSage、SVAM和DSGAT模块，通过图神经网络与注意力机制提升特征提取与目标检测能力。模型结合IoT技术实现数据实时采集与处理，优化资源配置与模型响应速度。实验表明，该模型在URPC 2020数据集上mAP达40.8%，在SeaDronesSee数据集上达41.5%，显著优于主流模型。该物联网增强方法不仅在高噪声、复杂背景中表现优异，还提升了系统整体效率与可扩展性，为水下目标检测提供了高效实用的解决方案，具有显著应用价值与发展前景。
原文摘要 · Abstract (English)
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.
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