AI-Powered Real-Time Runway Safety: UAV-Based Video Analysis with ICSO-Enhanced Deep Learning
DOI:
https://doi.org/10.22399/ijcesen.1184Keywords:
Runway Safety, Runway Crack, Runway Friction, Video Analysis, Flight Safety LandingAbstract
In the aviation sector, ensuring safe landings while prioritizing the safety of runways is crucial to prevent accidents and incidents during the landing phase of flights. However, many studies analyzing unsafe events, such as runway cracks or inadequate friction, often fail to quantify their impacts on flight safety during landing. In airport pavement management systems (APMS), the condition of the runway surface is a critical factor in ensuring the operational safety of aircraft during take-off and landing. Therefore, it is essential to provide pilots with reports on runway conditions, including measurements of surface performance, to support informed decision-making. To tackle these challenges, we propose a real-time automatic monitoring system for runway safety utilizing video analysis. Specifically, we employ a time-series analysis approach using the improved chameleon swarm optimization (ICSO) algorithm to mine runway surface characteristics from real-time video data captured by unmanned aerial vehicles (UAVs). Subsequently, we introduce the fuzzy reinforced polynomial neural network (FR-PNN) to detect risks in runway surface characteristics, enabling automatic monitoring to enhance the safety of aircraft landings. Finally, the effectiveness of the proposed system is validated using real-time videos obtained from Bechyne military airport, located in Bohemia. This system aims to improve runway safety by providing timely and accurate assessments of runway conditions, thereby facilitating safer landings for aircraft.
References
Vorobyeva, O., Bartok, J., Šišan, P., Nechaj, P., Gera, M., Kelemen, M., ...&Gaál, L. (2020). Assessing the contribution of data mining methods to avoid aircraft run-off from the runway to increase the safety and reduce the negative environmental impacts. International journal of environmental research and public health, 17(3), 796.
Gradolewski, D., Dziak, D., Kaniecki, D., Jaworski, A., Skakuj, M., &Kulesza, W. J. (2021). A runway safety system based on vertically oriented stereovision.Sensors, 21(4), 1464.
Bongo, M., &Seva, R. (2022). Effect of Fatigue in Air Traffic Controllers’ Workload, Situation Awareness, and Control Strategy. The International Journal of Aerospace Psychology, 32(1), 1-23.
Zhang, J., Zou, X., Wu, Q., Xie, F., & Liu, W. (2020). Empirical study of airport geofencing for unmanned aircraft operation based on flight track distribution. Transportation Research Part C: Emerging Technologies, 121, 102881.
Koroniotis, N., Moustafa, N., Schiliro, F., Gauravaram, P., &Janicke, H. (2020). A holistic review of cybersecurity and reliability perspectives in smart airports. IEEE Access, 8, 209802-209834.
Ruskin, K. J., Corvin, C., Rice, S., Richards, G., Winter, S. R., & Ruskin, A. C. (2021). Alarms, alerts, and warnings in air traffic control: an analysis of reports from the Aviation Safety Reporting System. Transportation research interdisciplinary perspectives, 12, 100502.
Malygin, I., Borodina, O., &Komashinsky, V. (2020). Decision support systems for ensuring safety of overland traffic at major airports.Transportation research procedia, 50, 422-429.
Khadonova, S. V., Ufimtsev, A. V., &Dymkova, S. S. (2020, October). “Digital Smart Airport” System Based on Innovative Navigation and Information Technologies. In 2020 International Conference on Engineering Management of Communication and Technology (EMCTECH) (pp. 1-6).IEEE.
Pestana, G., Reis, P., & da Silva, T. R. (2021). Smart Surveillance of Runway Conditions. In Intelligent Transport Systems, From Research and Development to the Market Uptake: 4th EAI International Conference, INTSYS 2020, Virtual Event, December 3, 2020, Proceedings 4 (pp. 252-270). Springer International Publishing.
Pramanik, P. K. D., Mukherjee, B., Pal, S., Upadhyaya, B. K., &Dutta, S. (2020). Ubiquitous manufacturing in the age of industry 4.0: A state-of-the-art primer. A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, 73-112.
Korkmaz, H., Filazoglu, E., &Ates, S. S. (2023). Enhancing airport apron safety through intelligent transportation systems: Proposed FEDA model.Safety science, 164, 106184.
Yousefi, Y., Karballaeezadeh, N., Moazami, D., SanaeiZahed, A., Mohammadzadeh S, D., &Mosavi, A. (2020). Improving aviation safety through modeling accident risk assessment of runway.International journal of environmental research and public health, 17(17), 6085.
Motlagh, N.H., Kortoçi, P., Su, X., Lovén, L., Hoel, H.K., Haugsvær, S.B., Srivastava, V., Gulbrandsen, C.F., Nurmi, P., & Tarkoma, S. (2023). Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey. IEEE Internet of Things Journal, 10, 21687-21704.
Iqbal, M. J., Iqbal, M. M., Ahmad, I., Alassafi, M. O., Alfakeeh, A. S., &Alhomoud, A. (2021). Real-time surveillance using deep learning. Security and Communication Networks, 2021, 1-17.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ...&Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1), 5.
Shin, W., Bu, S. J., & Cho, S. B. (2020). 3D-convolutional neural network with generative adversarial network and autoencoder for robust anomaly detection in video surveillance. International Journal of Neural Systems, 30(06), 2050034.
Puranik, T. G., Rodriguez, N., &Mavris, D. N. (2020). Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning. Transportation Research Part C: Emerging Technologies, 120, 102819.
Van Phat, T., Alam, S., Lilith, N., Tran, P. N., &Binh, N. T. (2021, July). Deep4air: A novel deep learning framework for airport airside surveillance. In 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6).IEEE.
Odisho, E. V., Truong, D., &Joslin, R. E. (2022). Applying machine learning to enhance runway safety through runway excursion risk mitigation. Journal of Aerospace Information Systems, 19(2), 98-112.
Xiang, Z., Yan, J., &Demir, I. (2020). A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water resources research, 56(1), e2019WR025326.
Li, X., Shang, J., Zheng, L., Wang, Q., Liu, D., Liu, X., ...& Sun, H. (2023). IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional Networks. IEEE Transactions on Intelligent Transportation Systems.
Farhadmanesh, M., Marković, N., &Rashidi, A. (2023). Automated video-based air traffic surveillance system for counting general aviation aircraft operations at non-towered airports. Transportation research record, 2677(3), 250-273..
Sun, Z. (2023, February). Research on the Application of Convolutional Neural Network Based on the YOLO Algorithm in Airport Intelligent Monitoring. In 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) (pp. 103-108).IEEE.
Jin, X., Peng, J., Jiao, X., & Hu, C. (2024, January). Construction and application of aircraft turnaround time research based on the video analysis platform. In International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023) (Vol. 12969, pp. 408-415). SPIE.
N. Soriano, A. Negahban, S. GokhanOzden and O. Ashour, (2023). An Open Source Video Analytics Tool for Analyzing Learner Navigation in Immersive Simulated Environments, 2023 Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON, Canada, pp. 472-483.
Han, J., Ren, Y. F., Brighente, A., & Conti, M. (2024). RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems. ACM Transactions on Intelligent Systems and Technology. 15(2);1 – 21 https://doi.org/10.1145/3641282
Balasubramanian, M., Ramyadevi, K., &Geetha, R. (2023). Deep transfer learning based real time face mask detection system with computer vision. Multimedia Tools and Applications, 1-20.
Thamilarasi V., Roselin. R., (2023). Application of Machine Learning in chest X-Ray images, International book & River publisher series in computing and information science and Technology, pp: 52-1.
Ran, J., Wan, H., Tang, W., & Kang, W. (2023, October). A Deep Learning-Based Flight Turnaround Record System.In 2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC) (pp. 896-903).IEEE.
Li, C., Sun, R., & Pan, X. (2023). Takeoff runway overrun risk assessment in aviation safety based on human pilot behavioral characteristics from real flight data. Safety science, 158, 105992.
Zhao, X. (2023, January). Design and Research of Airfield Navaid Lighting Monitoring System Based on Data Mining Algorithm. In International Conference on Innovative Computing (pp. 96-102). Singapore: Springer Nature Singapore.
AY, S. (2024). Vehicle Detection And Vehicle Tracking Applications On Traffic Video Surveillance Systems: A systematic literature review. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.629
S.P. Lalitha, & A. Murugan. (2024). Performance Analysis of Priority Generation System for Multimedia Video using ANFIS Classifier. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.707
GÜNGÖRDÜ, G., & İMAMOĞLU, B. (2024). Occupational Health and Safety Risks in Aircraft Arresting Barrier Systems. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.748
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
