TY - GEN
T1 - Real-Time Drone Signal Recognition System Based on Improved YOLOv5 in Complex Electromagnetic Environments
AU - Qian, Haitao
AU - Li, Bin
AU - Li, Silong
AU - Zhang, Ruonan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In this paper, we proposed a method and testing system for identifying unmanned aerial vehicle (UAV) electromagnetic signals in complex environments. We utilized signals collected in authentic environments alongside publicly available data to construct a dataset and optimized images using contrast enhancement techniques. We improved the YOLOv5 model to enhance detection accuracy and built a complete system based on this improved model. In real-world tests, our model achieved an accuracy of 90.7% and a recall rate of 87.4%. The system can identify UAVs within 4 milliseconds (ms) and output results at a speed of 100 frames per second (fps). The results indicate that the performance of the improved algorithm surpasses that of traditional methods, and the system demonstrates excellent real-time capability.
AB - In this paper, we proposed a method and testing system for identifying unmanned aerial vehicle (UAV) electromagnetic signals in complex environments. We utilized signals collected in authentic environments alongside publicly available data to construct a dataset and optimized images using contrast enhancement techniques. We improved the YOLOv5 model to enhance detection accuracy and built a complete system based on this improved model. In real-world tests, our model achieved an accuracy of 90.7% and a recall rate of 87.4%. The system can identify UAVs within 4 milliseconds (ms) and output results at a speed of 100 frames per second (fps). The results indicate that the performance of the improved algorithm surpasses that of traditional methods, and the system demonstrates excellent real-time capability.
KW - Complex environments
KW - Identifying unmanned aerial vehicle
KW - Improved YOLOv5 model
KW - Real-time capability
UR - http://www.scopus.com/inward/record.url?scp=105007223972&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2767-7_41
DO - 10.1007/978-981-96-2767-7_41
M3 - 会议稿件
AN - SCOPUS:105007223972
SN - 9789819627660
T3 - Lecture Notes in Electrical Engineering
SP - 429
EP - 439
BT - Proceedings of the 3rd International Conference on Internet of Things, Communication and Intelligent Technology - Internet of Things and Communication
A2 - Dong, Jian
A2 - Zhang, Long
A2 - Zheng, Tongxing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024
Y2 - 29 June 2024 through 1 July 2024
ER -