Detection of dangerous water area during UAV autonomous landing

Shaoshan Liu, Jianmei Song, Haoping She*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Aiming at the problem of water dangerous area detection faced by UAV during emergency autonomous landing, the features of water dangerous area are extracted from the image by neural network, the texture features of the image are obtained by HOG algorithm, and the features extracted by neural network and texture features are classified by support vector machine method (SVM). Then, the classifier is trained based on color features and regional texture features to detect the specific location of water hazard areas in the image. The experiment shows that the method has a good result in detecting the dangerous area of water during UAV autonomous landing, and the detection accuracy can reach more than 90%.

源语言英语
主期刊名Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
出版商Institute of Electrical and Electronics Engineers Inc.
4609-4615
页数7
ISBN(电子版)9798350334722
DOI
出版状态已出版 - 2023
活动35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, 中国
期限: 20 5月 202322 5月 2023

出版系列

姓名Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

会议

会议35th Chinese Control and Decision Conference, CCDC 2023
国家/地区中国
Yichang
时期20/05/2322/05/23

指纹

探究 'Detection of dangerous water area during UAV autonomous landing' 的科研主题。它们共同构成独一无二的指纹。

引用此