基于深度学习的无人机航拍目标检测研究综述

doi:10.7527/S1000-6893.2020.24519

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基于深度学习的无人机航拍目标检测研究综述

江波, 屈若锟, 李彦冬, 李诚龙

中国民用航空飞行学院, 广汉 618307

收稿日期:2020-07-09 修回日期:2020-07-20 发布日期:2020-08-17
通讯作者: 屈若锟 E-mail:qurk0226@qq.com
基金资助:
四川省科技计划重点研发项目（2019YFG0308）；四川省2018-2020年高等教育人才培养质量和教学改革项目（JG2018-325）；四川省大学生创新创业训练计划项目（S202010624029）；中国民用航空飞行学院科研项目（J2008-78）；中国民航飞行学院面上项目（J2020-078）

Object detection in UAV imagery based on deep learning: Review

JIANG Bo, QU Ruokun, LI Yandong, LI Chenglong

Civil Aviation Flight University of China, Guanghan 618307, China

Received:2020-07-09 Revised:2020-07-20 Published:2020-08-17
Supported by:
Sichuan Province Science and Technology Plan-Key Research and Development (2019YFG0308); Sichuan Province Talents Fostering Quality and Teaching Reform Program of Higher Education in 2018-2020 (JG2018-325); Sichuan province College Students' Innovative Entrepreneurial Training Plan Program (S202010624029); Research Program of Civil Aviation Flight University of China (J2008-78); General Program by Civil Aviation Flight University of China (J2020-078)

摘要/Abstract

摘要： 目标检测是提高无人机（UAV）感知能力的关键技术之一，其研究对于无人机的应用有着重要意义。与基于手工特征的传统方法相比，基于卷积神经网络的深度学习方法具有强大的特征学习和表达能力，成为目前目标检测任务的主流算法。近年来，目标检测技术已经在自然场景图像上取得了一系列突破性进展，在无人机领域的研究也逐渐成为热点。首先系统阐述了基于深度学习的目标检测算法的研究进展，并总结了相关算法的优缺点。对常见的航空影像数据集进行了梳理并介绍了迁移学习的方法；从无人机影像背景复杂、目标较小、视场大、目标具有旋转性的特点出发，对无人机目标检测在近期的研究进行了归纳和分析。最后讨论了存在的问题和未来可能的发展方向。

关键词: 目标检测, 无人机影像, 卷积神经网络, 计算机视觉, 深度学习, 迁移学习

Abstract: Object detection is one of the key technologies in improving the autonomous sensing ability of Unmanned Aerial Vehicles (UAVs). Research on object detection is of critical significance in UAV applications. Compared with traditional methods based on manual features, deep learning based on the convolutional neural network has a powerful capability of feature learning and expression, therefore becoming the mainstream algorithm in object detection. In recent years, object detection research has achieved a series breakthrough in the field of natural scene and the research in UAVs has increasingly become a hotspot simultaneously. This paper reviews the research progress of object detection algorithms based on deep learning, summarizing their advantages and disadvantages. Then, some typical aerial image datasets and the method of transfer learning are introduced, and relevant algorithms are analyzed aiming at the complex background, small and rotating objects, large fields of view in UAV imagery. The existing problems and possible future development directions are finally discussed.

Key words: object detection, UAV imagery, convolution neural networks, computer vision, deep learning, transfer learning

中图分类号:

V279
TP181

江波, 屈若锟, 李彦冬, 李诚龙. 基于深度学习的无人机航拍目标检测研究综述[J]. 航空学报, 2021, 42(4): 524519-524519.

JIANG Bo, QU Ruokun, LI Yandong, LI Chenglong. Object detection in UAV imagery based on deep learning: Review[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524519-524519.

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基于深度学习的无人机航拍目标检测研究综述

Object detection in UAV imagery based on deep learning: Review

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