@inproceedings{1cd844083f474e50a661d834be19309a,
title = "Detection of dangerous water area during UAV autonomous landing",
abstract = "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%.",
keywords = "UAV, neural network, support vector machine, water detection",
author = "Shaoshan Liu and Jianmei Song and Haoping She",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",
year = "2023",
doi = "10.1109/CCDC58219.2023.10326606",
language = "English",
series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4609--4615",
booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
address = "United States",
}