Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
Author:
Affiliation:
1. Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China
2. Kunming Institute of Physics, Kunming 650223, China
3. Yunnan Airport Group Co., Ltd., Kunming 650500, China
Abstract
Funder
National Natural Science Foundation of China
Publisher
MDPI AG
Link
https://www.mdpi.com/2076-3417/14/5/2162/pdf
Reference35 articles.
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2. Jain, S., Prasad, M., and Gutpa, R. (2022, January 4–5). A Comparison of Manual and Automotive FOD Detection Systems at Airport Runways. Proceedings of the 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.
3. Airport runway foreign object debris detection system based on arc-scanning SAR technology;Wang;IEEE Trans. Geosci. Remote Sens.,2022
4. Qin, F., Bu, X., Liu, Y., Liang, X., and Xin, J. (2021). Foreign object debris automatic target detection for millimeter-wave surveillance radar. Sensors, 21.
5. Yang, X., Huo, K., Zhang, X., Jiang, W., and Chen, Y. (2019). A clutter-analysis-based STAP for moving FOD detection on runways. Sensors, 19.
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