将实时视频与海图数据融合，精准定位海上浮标。
Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision
- 用基于Transformer的神经网络直接匹配图像中的浮标与海图标记。
- 在真实海况下，目标定位与关联准确率显著优于基线方法。
- 适合航海导航、智能船舶系统开发者参考。
本文提出一种新方法，通过融合实时视觉数据与海图信息来增强海洋视觉能力。系统通过精确匹配检测到的航标（如浮标）与其在海图数据中的对应表示，将海图数据叠加到实时视频流上。为实现鲁棒匹配，引入基于Transformer的端到端神经网络，预测浮标查询的边界框和置信度，实现图像域检测与世界坐标海图标记的直接匹配。在真实海景数据集上的实验表明，该方法在动态复杂环境中显著提升了目标定位与关联的准确性。对比基线方法，包括基于射线投射的模型（通过相机投影估计浮标位置）以及扩展了距离估计模块的YOLOv7网络，本方法表现更优。
原文摘要 · Abstract (English)
This paper presents a novel approach to enhancing marine vision by fusing real-time visual data with chart information. Our system overlays nautical chart data onto live video feeds by accurately matching detected navigational aids, such as buoys, with their corresponding representations in chart data. To achieve robust association, we introduce a transformer-based end-to-end neural network that predicts bounding boxes and confidence scores for buoy queries, enabling the direct matching of image-domain detections with world-space chart markers. The proposed method is compared against baseline approaches, including a ray-casting model that estimates buoy positions via camera projection and a YOLOv7-based network extended with a distance estimation module. Experimental results on a dataset of real-world maritime scenes demonstrate that our approach significantly improves object localization and association accuracy in dynamic and challenging environments.
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