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With the advancement of maritime unmanned aerial vehicles (UAVs) and deeplearning technologies, the application of UAV-based object detection has becomeincreasingly significant in the fields of maritime industry and oceanengineering. Endowed with intelligent sensing capabilities, the maritime UAVsenable effective and efficient maritime surveillance. To further promote thedevelopment of maritime UAV-based object detection, this paper provides acomprehensive review of challenges, relative methods, and UAV aerial datasets.Specifically, in this work, we first briefly summarize four challenges forobject detection on maritime UAVs, i.e., object feature diversity, devicelimitation, maritime environment variability, and dataset scarcity. We thenfocus on computational methods to improve maritime UAV-based object detectionperformance in terms of scale-aware, small object detection, view-aware,rotated object detection, lightweight methods, and others. Next, we review theUAV aerial image/video datasets and propose a maritime UAV aerial dataset namedMS2ship for ship detection. Furthermore, we conduct a series of experiments topresent the performance evaluation and robustness analysis of object detectionmethods on maritime datasets. Eventually, we give the discussion and outlook onfuture works for maritime UAV-based object detection. The MS2ship dataset isavailable at\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.

Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons