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基于深度学习的无人机目标检测研究综述
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Abstract:
无人机具有体积小、灵活性强、航拍视野广等特点，广泛应用于警用巡查、城市交通监管、天气监测、电力巡检、应急救援救灾等行业。近年来，随着计算机视觉领域的蓬勃发展，基于深度学习的目标检测技术逐渐应用于无人机领域，并不断得到改进和加强。本文系统性地阐述了基于深度学习的目标检测技术发展历程和研究现状。针对现阶段无人机航拍影像小目标多、背景复杂、目标尺度变化大的特性，归纳和分析了近期对无人机目标检测的相关研究。最后，展望了基于深度学习的无人机目标检测技术的未来发展趋势。
The UAV has the characteristics of small size, strong flexibility, wide range of aerial photography, and is widely used in police patrol, urban traffic supervision, weather monitoring, electric power patrol, emergency rescue and disaster relief and other industries. In recent years, with the vigorous development of the field of computer vision, the target detection technology based on deep learning has been gradually applied to the field of unmanned aerial vehicles, and has been continuously improved and strengthened. This paper first systematically expounds the development history and research status of object detection technology based on deep learning. Aiming at the characteristics of many small targets, complex background and large change of target scale in UAV aerial photography at this stage, the optimization methods of UAV target detection in recent times are summarized and analyzed. Finally, the future development trend of UAV target detection technology based on deep learning is prospected.
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