Multi-obstacle detection based on monocular vision for UAV

被引：4

作者：

She, Xingbin ^{[1
]}

Huang, Deqing ^{[1
]}

Song, Chenjian ^{[1
]}

Qin, Na ^{[1
]}

Zhou, Taoyuan ^{[2
]}

机构：

[1] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Peoples R China

[2] Wuxi Comner Med Technol Co Ltd, Wuxi, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE 2021 IEEE 16TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2021) | 2021年

关键词：

feature point extraction; visual expansion; YOLOv3; multi-obstacle detection;

D O I：

10.1109/ICIEA51954.2021.9516384

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Obsiade detection is the key problem of UAV autonomous obstacle avoidance. This paper proposes a multi-obstacle detection method based on monocular vision. The proposed method uses YOLOv3 to detect the suspected obstacle regions. SURF algorithm is used to extract and match the feature points. The principle of visual expansion is used to confirm the obstacle. Multithreading programming technology is introduced to reduce the running time or the system. Simulation experiments are conducted to verify the effectiveness of the proposed method.

引用

页码：1067 / 1072

页数：6

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