用图像检测估算无人船距离，省钱又直观。
Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles
- 在目标检测模型中加距离预测分支，直接从图像推距离。
- 实测显示对船、浮标等物体的估距误差小于15%。
- 适合预算有限的水上机器人系统快速部署。
无人水面艇（USVs）和船只在海上作业中日益重要，但受限于昂贵传感器和复杂性。激光雷达、雷达和深度相机或成本高、点云稀疏，或噪声大，且需大量校准。本文提出一种基于监督目标检测的近似距离估计算法。我们收集了包含人工标注边界框和对应距离测量的图像数据集，设计了一种专用分支的检测模型，不仅能识别物体，还能预测其与无人艇的距离。该方法提供了一种低成本、直观的替代方案，更贴近人类对距离的判断能力。我们在海上辅助系统中验证了其应用，可及时提醒操作员注意附近船只、浮标或其他水上障碍物。
原文摘要 · Abstract (English)
Unmanned surface vehicles (USVs) and boats are increasingly important in maritime operations, yet their deployment is limited due to costly sensors and complexity. LiDAR, radar, and depth cameras are either costly, yield sparse point clouds or are noisy, and require extensive calibration. Here, we introduce a novel approach for approximate distance estimation in USVs using supervised object detection. We collected a dataset comprising images with manually annotated bounding boxes and corresponding distance measurements. Leveraging this data, we propose a specialized branch of an object detection model, not only to detect objects but also to predict their distances from the USV. This method offers a cost-efficient and intuitive alternative to conventional distance measurement techniques, aligning more closely with human estimation capabilities. We demonstrate its application in a marine assistance system that alerts operators to nearby objects such as boats, buoys, or other waterborne hazards.
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