@inproceedings{0f304e442484494b87a07405fdb8b334,
title = "Low Altitude Small UAV Detection Based on YOLO model",
abstract = "Low altitude small UAV recently becomes a hot technology with rapid development and comprehensive application which raising many risks in Low-altitude airspace. Considering the characteristics of low altitude small UAV, previous approaches like radar and radio are insufficient, this paper is devoted to the detection of small UAV by using images from low-cost cameras. Method of detecting low altitude small UAV based on YOLO model with two neural networks, ResNet and DenseNet, is designed and performed. One small dataset is established accordingly. Experiment on the dataset shows that the detection method with YOLO model can make contributions to the low altitude UAV detection in complicated environments.",
keywords = "Detection, Low Attitude UAV, Neural Network, YOLO",
author = "Xiyu Yuan and Jie Xia and Jiang Wu and Shi, \{Jin Xu\} and Lin Deng",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188588",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7362--7366",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "美国",
}