With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.

翻译：随着海事无人机与深度学习技术的发展，基于无人机的目标检测在海事工业与海洋工程领域中的应用日益重要。凭借智能感知能力，海事无人机能够实现高效的海上监控。为进一步推动海事无人机目标检测的发展，本文系统综述了相关挑战、方法及无人机航拍数据集。具体而言，我们首先简要归纳了海事无人机目标检测面临的四大挑战，即目标特征多样性、设备局限性、海洋环境多变性与数据集稀缺性。随后聚焦于提升海事无人机目标检测性能的计算方法，涵盖尺度感知、小目标检测、视角感知、旋转目标检测、轻量级方法等方向。接着，我们梳理了无人机航拍图像/视频数据集，并提出名为MS2ship的海事无人机航拍船舶检测专用数据集。此外，我们通过系列实验对海事数据集上的目标检测方法进行了性能评估与鲁棒性分析。最后，对海事无人机目标检测的未来工作进行了讨论与展望。MS2ship数据集可在\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}获取。