Deep learning-based foreign object detection method for aviation runways

[1] Hu, F. (2019). Railway’s Diverse Appeal Rail travel has emerged as the preferred choice for Chinese tourists, young and old. ChinAfrica. Search in Google Scholar

[2] Tarlow, P. E. (2014). Transportation: Travel by Air, Car, and Train. Tourism Security, 167. Search in Google Scholar

[3] Mazon, J., Rojas, J. I., Lozano, M., Pino, D., Prats, X., & Miglietta, M. M. (2018). Influence of meteorological phenomena on worldwide aircraft accidents, 1967–2010. Meteorological applications, 25(2), 236-245. Search in Google Scholar

[4] Wang, H., & Wang, X. G. (2011). Key Technologies of Radar for Foreign Objects Debris(FOD) Detection on Runways. Dianxun Jishu/Telecommunications Engineering, 51(2), 7-10. Search in Google Scholar

[5] Dewulf, W., Forbes, S., & Li, Y. (2022). The effect of accidents on aircraft manufacturers’ competition. In The Air Transportation Industry (pp. 411-431). Elsevier. Search in Google Scholar

[6] Wang, J. (2019). A Hybrid Airport Runway ‘s Foreign Object Debris Detection System. Science & Technology Vision. Search in Google Scholar

[7] Liu, S., Yu, N. (2019). Experimental Study on Temperature Control of Optical Detection Turntable of Airport Runway Foreign Body Detection System. Refrigeration & Air Conditioning. Search in Google Scholar

[8] Zheng, J. (2018) Design of Airport Runway Foreign Body Detection System Based on Polarization Imaging. Digital Technology & Application. Search in Google Scholar

[9] Fong, C H. (2018). Airport Security-Improved Detection. Asian Defence Journal, 18-19. Search in Google Scholar

[10] Beasley P, Binns G, Hodges R D, et al. (2005). Tarsier/spl R/, a millimetre wave radar for airport runway debris detection//First European Radar Conference. EURAD. IEEE, 2005. Search in Google Scholar

[11] Yong, H. E., Sun C F. (2016). Research and Application of Airport Runway’s Foreign Object Debris Monitoring System Based on Radar Detecting. Measurement & Control Technology. Search in Google Scholar

[12] Warok, P. Millimeter wave radar network for foreign object detection on runways. Poster. Search in Google Scholar

[13] Yu, L. I., Xiao, G. (2011). Study and design on FOD detection and surveillance system for airport runway. Laser & Infrared, 41(8), 909-915. Search in Google Scholar

[14] Xiao-qi, Yang, Kai, et al. (2018). An Improvement of Kulemin Models for FOD Detection. Search in Google Scholar

[15] Beasley, P, Binns, G., Hodge,s R. D., et al. (2005). Tarsier/spl R/, a millimetre wave radar for airport runway debris detection. First European Radar Conference. EURAD. IEEE, 2005. Search in Google Scholar

[16] Woodworth, E. (2010). Procedures for FOD Detection System Performance Assessments: Radar-Based and Dual Sensor Systems. 2010. Search in Google Scholar

[17] Shuang-yun, X., Gan-lin, et al. (2018). Traffic Signal Light Optimization Control Based on Fuzzy Control and CCD Camera Technology. Search in Google Scholar

[18] Wolfram, I. (2018). How Do I Perform CCD Image Reduction with Mathematica 4.2? Search in Google Scholar

[19] Halama S, Dubielzig T, Orlowski N, et al. (2022). Real-time capable CCD-based individual trapped-ion qubit measurement. Search in Google Scholar

[20] Vogel, B. (2019). Kuala Lumpur begins FOD detection trials. Jane’s airport review: IHS Jane’s airport review. (5), 31. Search in Google Scholar

[21] Herricks, E. (2010). Performance Assessment of FOD Detection Systems. Search in Google Scholar

[22] Zhang, D., Yan, X. (2019). Brief Analysis of Airport Runway FOD Monitoring System. Urban Roads Bridges & Flood Control. Search in Google Scholar

[23] Ping-Wei, L. I. (2012). Situation and Development of the Millimeter Wave Radar in Airport Runway FOD Detection. Journal of Microwaves. Search in Google Scholar

[24] Kohmura, A. Foreign Objects and Debris Detection System on Runways Using Millimeter Wave Radar Connected by Radio over Fiber in 90 GHz. Search in Google Scholar

[25] Dang, M. T. (2011). Airport safety monitoring. International University Hcmc Vietnam. Search in Google Scholar

[26] Zeegers M T., Leeuwen T V., Pelt D M., et al. (2022). A tomographic workflow to enable deep learning for X-ray based foreign object detection. arXiv e-prints. Search in Google Scholar

[27] Chen, Y., Tong, S., Lu, X., et al. (2021). A Semi-Supervised Railway Foreign Object Detection Method Based on GAN. Search in Google Scholar

[28] Du, X. (2017). Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis. University of Maryland, College Park. Search in Google Scholar

[29] Kang, K. (2017). Intelligent Video Analysis with Deep Learning. The Chinese University of Hong Kong (Hong Kong). Search in Google Scholar

[30] Foreign Object Detection of Electric Transmission Line with Dynamic Federated Learning. Search in Google Scholar

[31] Jürgen Schmidhuber. (2015). Deep learning in neural networks. Neural Netw. Search in Google Scholar