无人机在无GPS环境下靠视觉识别海上目标船，实现自主搜寻定位。
Aerial Maritime Vessel Detection and Identification
- 用YOLOv8检测视野内所有船只，结合特征匹配与颜色直方图判断目标
- 在真实场景中实现无卫星导航下的自动搜寻与目标定位，精度受视角影响小
- 适合无人艇搜救、反潜等无信号环境下的自主任务，对算力要求低
在无法使用全球导航卫星系统（GNSS）的环境中，实现自主海上监视与目标船舶识别对于搜救和威胁探测至关重要。当目标船仅通过视觉线索描述且无最后已知位置时，无人飞行器（UAV）必须依赖机载视觉，在严格计算约束下扫描大范围区域。为此，我们采用YOLOv8目标检测模型检测视场内所有船只，并通过特征匹配与色相直方图距离分析判断是否为待识别目标。一旦发现，利用简单几何原理进行目标精确定位。我们在MBZIRC2023竞赛中开展真实实验，将该方法集成于完全自主的无GNSS导航系统。同时评估了视角对检测精度与定位精度的影响，并与理想情况下的基准方法进行对比。
原文摘要 · Abstract (English)
Autonomous maritime surveillance and target vessel identification in environments where Global Navigation Satellite Systems (GNSS) are not available is critical for a number of applications such as search and rescue and threat detection. When the target vessel is only described by visual cues and its last known position is not available, unmanned aerial vehicles (UAVs) must rely solely on on-board vision to scan a large search area under strict computational constraints. To address this challenge, we leverage the YOLOv8 object detection model to detect all vessels in the field of view. We then apply feature matching and hue histogram distance analysis to determine whether any detected vessel corresponds to the target. When found, we localize the target using simple geometric principles. We demonstrate the proposed method in real-world experiments during the MBZIRC2023 competition, integrated into a fully autonomous system with GNSS-denied navigation. We also evaluate the impact of perspective on detection accuracy and localization precision and compare it with the oracle approach.
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