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

Abstract

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

CLC Number:

V279
TP181

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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Object detection in UAV imagery based on deep learning: Review

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