@inproceedings{ce6ae54671a9400daf3bfaada31a5a8e,
title = "Detection and Tracking of Intruding Unmanned Aerial Vehicles Based on Dual-modality",
abstract = "The unmanned aerial vehicles detection and tracking system is of vital significance in the current military and civil aviation fields. The research on this system is inseparable from object detection and object tracking algorithms. This paper first introduces the self-collected dual-modal dataset and briefly explains the data annotation tool. Subsequently, six mainstreams deep learning based object detection algorithms are tested on this dataset, and the basic detection results of these detectors are obtained. To further enhance the detection performance, we improved the YOLOv8 algorithm with the best performance by integrating two types of feature pyramids into it. After the improvement, it can be clearly seen that the detection effect has been enhanced. Finally, this paper elaborates on how to combine the object detector and the object tracker to construct a complete system.",
keywords = "Dual-modality, Object Detection, Unmanned Aerial Vehicles, Visual Tracking",
author = "Ruiheng Zhang and Ruoxi Zhang and Yang Lei and Yu Zhang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 5th International Conference on Big Data Engineering and Education, BDEE 2025 ; Conference date: 11-04-2025 Through 13-04-2025",
year = "2025",
doi = "10.1109/BDEE67464.2025.00023",
language = "English",
series = "Proceedings - 5th International Conference on Big Data Engineering and Education, BDEE 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "107--111",
booktitle = "Proceedings - 5th International Conference on Big Data Engineering and Education, BDEE 2025",
address = "United States",
}