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：随着海上无人飞行器（UAVs）与深度学习技术的发展，基于UAV的目标检测在海洋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，海上UAV能够实现高效的海上监控。为进一步推动基于海上UAV的目标检测发展，本文系统梳理了相关挑战、方法及UAV航拍数据集。具体而言，本文首先简要总结了海上UAV目标检测的四大挑战：目标特征多样性、设备局限性、海洋环境多变性及数据集稀缺性。随后，我们聚焦于提升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、轻量化方法及其他技术。接着，我们回顾了UAV航拍图像/视频数据集，并提出面向船舶检测的海上UAV航拍数据集MS2ship。此外，我们通过系列实验评估了目标检测方法在海洋数据集上的性能与鲁棒性。最后，我们对未来研究方向进行了讨论与展望。MS2ship数据集可通过\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}获取。