An Improved YOLO Algorithm for Identifying Civil Aviation Suppression Interference Signals
1. College of Aviation Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China
2. Civil Aviation Southwest Air Traffic Control Bureau Maintenance Center, Chengdu 610200, China
| Abstract: | In view of the current situation that there are many types of civil aviation interference sources and the interference identification algorithm is relatively scarce in the field of interference detection, an improved YOLOv7-ESC interference identification algorithm is proposed. Firstly, four common suppression interferences in civil aviation signals are modeled and an interference data set is constructed; secondly, continuous wavelet transform (CWT) is introduced as a time-frequency domain processing and analysis method to highlight the time-frequency feature information of the signal; then, the ECA attention mechanism, SE attention mechanism, and CBAM attention mechanism are integrated into the YOLOv7 backbone network to enhance the signal feature extraction capability; finally, a YOLOv7-ESC algorithm that integrates three attention mechanisms is studied to accurately classify and identify different interference signals. Experimental results show that compared with the traditional YOLOv7, the recognition accuracy (P) of the YOLOv7-ESC model increased from 0.930 to 0.986, an increase of 6.0%; the mean average precision (mAP) increased from 0.975 to 0.982, an increase of 0.7%; and the recall rate (R) increased from 0.965 to 0.989, an increase of 2.5%. The YOLOv7-ESC model has obvious advantages in interference identification and anti-interference capabilities, and has broad application prospects in the field of accurate investigation and identification of civil aviation interference sources. |
| Keywords: | Deep Learning; YOLOv7; Civil Aviation Interference; Attention Mechanism; Signal Recognition |
| DOI: | 10.57237/j.cst.2025.02.002 |
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Received: 11 March 2025
Accepted: 21 April 2025
Published Online: 20 May 2025
Funding
| 1. | 民航飞行技术与飞行安全重点实验室研究项目 (FZ2022ZZ03; FZ2022ZX46) |
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