空中海上船舶检测与识别
Aerial Maritime Vessel Detection and Identification
AI总结：
针对无GNSS环境下无人机海上目标识别问题，提出基于YOLOv8检测、特征匹配与色调直方图分析的目标船舶定位方法，并在MBZIRC2023竞赛中验证了其有效性。
摘要：
在GNSS不可用的环境中，自主海上监视和目标船舶识别对搜救和威胁检测等应用至关重要。当目标船舶仅靠视觉线索描述且最后已知位置不可用时，无人机必须仅依赖机载视觉在严格计算约束下扫描大片搜索区域。为应对此挑战，我们利用YOLOv8目标检测模型检测视野内所有船舶。随后应用特征匹配和色调直方图距离分析，判断任何被检测船舶是否对应目标。找到目标后，我们利用简单几何原理进行定位。我们在MBZIRC2023竞赛的真实实验中演示了该方法，将其集成到具有GNSS拒止导航的完全自主系统中。我们还评估了视角对检测精度和定位精度的影响，并与oracle方法进行了比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
