基于深度学习的水上无人机图像目标检测：综述与实验比较
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
AI总结：
本文综述水上无人机目标检测的挑战、方法与数据集，提出MS2ship数据集，并通过实验评估方法性能，展望未来方向。
摘要：
随着水上无人机（UAVs）和深度学习技术的进步，基于UAV的目标检测在海洋工业和海洋工程领域的应用变得越来越重要。水上无人机具备智能感知能力，能够实现高效且有效的海上监视。为了进一步推动基于水上UAV的目标检测的发展，本文对挑战、相关方法和UAV航拍数据集进行了全面综述。具体而言，在本工作中，我们首先简要总结了水上UAV目标检测面临的四个挑战，即目标特征多样性、设备限制、海洋环境多变性和数据集稀缺性。然后，我们重点关注提高基于水上UAV目标检测性能的计算方法，包括尺度感知、小目标检测、视角感知、旋转目标检测、轻量级方法及其他方法。接下来，我们回顾了UAV航拍图像/视频数据集，并提出了一个名为MS2ship的水上UAV航拍数据集用于船舶检测。此外，我们进行了一系列实验，以呈现目标检测方法在水上数据集上的性能评估和鲁棒性分析。最后，我们对基于水上UAV目标检测的未来工作进行了讨论和展望。MS2ship数据集可在\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}获取。
英文摘要：
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}.
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。
