{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:18:48Z","timestamp":1791559128083,"version":"4.3.4"},"reference-count":22,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TRC, MOHERI, OMAN","award":["BFP\/RGP\/ICT\/22\/003"],"award-info":[{"award-number":["BFP\/RGP\/ICT\/22\/003"]}]},{"name":"TRC, OHERI, OMAN","award":["BFP\/RGP\/ICT\/22\/003"],"award-info":[{"award-number":["BFP\/RGP\/ICT\/22\/003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model\u2019s architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model\u2019s focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model\u2019s applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.<\/jats:p>","DOI":"10.3390\/s24175455","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents"],"prefix":"10.3390","volume":"24","author":[{"given":"Najiba","family":"Said Hamed Alzadjali","sequence":"first","affiliation":[{"name":"College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sundaravadivazhagan","family":"Balasubaramainan","sequence":"additional","affiliation":[{"name":"College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Savarimuthu","sequence":"additional","affiliation":[{"name":"College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Rances","sequence":"additional","affiliation":[{"name":"College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. 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Bird Strike in Aviation: Statistics, Analysis and Management, John Wiley & Sons.","DOI":"10.1002\/9781119529835"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.61089\/aot2024.h7j32562","article-title":"The Multidimensional Threats of Unmanned Aerial Systems: Exploring Biomechanical, Technical, Operational, and Legal Solutions for Ensuring Safety and Security","volume":"69","author":"Perz","year":"2024","journal-title":"Arch. Transp."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102296","DOI":"10.1016\/j.jairtraman.2022.102296","article-title":"A novel integrated risk management method for airport operations","volume":"105","author":"Sivakumar","year":"2022","journal-title":"J. Air Transp. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1139\/er-2015-0039","article-title":"The effects of modern war and military activities on biodiversity and the environment","volume":"23","author":"Lawrence","year":"2015","journal-title":"Environ. Rev."},{"key":"ref_8","first-page":"469","article-title":"It\u2019s a Bird, It\u2019s a Plane, It\u2019s a Problem: The Impact of Bird Strikes on the Civil Aviation Industry","volume":"75","author":"Thompson","year":"2010","journal-title":"J. Air L. Com."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/j.1749-6632.2010.05451.x","article-title":"Ecology of avian influenza viruses in a changing world","volume":"1195","author":"Vandegrift","year":"2010","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114386","DOI":"10.1016\/j.rser.2024.114386","article-title":"Making eco-sustainable floating offshore wind farms: Siting, mitigations, and compensations","volume":"197","author":"Danovaro","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nimmagadda, S., Sivakumar, S., Kumar, N., and Haritha, D. (2020, January 23\u201324). Predicting airline crash due to birds strike using machine learning. Proceedings of the International Conference on Smart Structures and Systems (ICSSS), Chennai, India.","DOI":"10.1109\/ICSSS49621.2020.9202137"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, Y., Cai, X., Wang, P., Huang, Z., Li, D., Shao, Y., and Wang, S. (2022, January 4\u20138). Airbirds: A large-scale challenging dataset for bird strike prevention in real-world airports. Proceedings of the Asian Conference on Computer Vision, Macau, China.","DOI":"10.1007\/978-3-031-26348-4_24"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109355","DOI":"10.1016\/j.dib.2023.109355","article-title":"YOLO-based segmented dataset for drone vs. bird detection for deep and machine learning algorithms","volume":"50","author":"Shandilya","year":"2023","journal-title":"Data Brief"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hong, S.-J., Han, Y., Kim, S.-Y., Lee, A.-Y., and Kim, G. (2019). Application of deep-learning methods to bird detection using unmanned aerial vehicle imagery. Sensors, 19.","DOI":"10.3390\/s19071651"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, K., Wen, L., Li, G., Bo, L., and Huang, Q. (2019, January 15\u201320). Spatiotemporal cnn for video object segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00147"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., and Sorkine-Hornung, A. (2016, January 27\u201330). A benchmark dataset and evaluation methodology for video object segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.85"},{"key":"ref_17","unstructured":"Pont-Tuset, J., Perazzi, F., Caelles, S., Arbel\u00e1ez, P., Sorkine-Hornung, A., and Van Gool, L. (2017). The 2017 davis challenge on video object segmentation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Prest, A., Leistner, C., Civera, J., Schmid, C., and Ferrari, V. (2012, January 16\u201321). Learning object class detectors from weakly annotated video. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248065"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jain, S.D., and Grauman, K. (2014, January 6\u201312). Supervoxel-consistent foreground propagation in video. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings Part IV 13.","DOI":"10.1007\/978-3-319-10593-2_43"},{"key":"ref_20","first-page":"1","article-title":"3D-STCNN: Spatiotemporal Convolutional Neural Network based on EEG 3D features for detecting driving fatigue","volume":"2","author":"Peng","year":"2024","journal-title":"J. Data Sci. Intell. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110089","DOI":"10.1016\/j.ress.2024.110089","article-title":"A novel hybrid STL-transformer-ARIMA architecture for aviation failure events prediction","volume":"246","author":"Zeng","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_22","unstructured":"Aditya, S., Shandilya, S.K., Datta, A., Yemets, K., and Nagar, A. (2023, December 05). Segmented Dataset Based on YOLOv7 for Drone vs. Bird Identification for Deep and Machine Learning Algorithms. Mendeley Data, 2023, V3. Available online: https:\/\/data.mendeley.com\/datasets\/6ghdz52pd7\/5."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5455\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:41:57Z","timestamp":1760110917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,23]]},"references-count":22,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175455"],"URL":"https:\/\/doi.org\/10.3390\/s24175455","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,23]]}}}