دورية أكاديمية
A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents.
| العنوان: | A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents. |
|---|---|
| المؤلفون: | Said Hamed Alzadjail N; College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman., Balasubaramainan S; College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman., Savarimuthu C; College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman., Rances EO; College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Aug 23; Vol. 24 (17). Date of Electronic Publication: 2024 Aug 23. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: | Deep Learning* , Birds*/physiology , Aircraft* , Neural Networks, Computer*, Animals ; Accidents, Aviation/prevention & control ; Image Processing, Computer-Assisted/methods ; Airports ; Flight, Animal/physiology ; Algorithms |
| مستخلص: | 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's 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's 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's 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. |
| References: | Ann N Y Acad Sci. 2010 May;1195:113-28. (PMID: 20536820) Sensors (Basel). 2019 Apr 06;19(7):. (PMID: 30959913) Data Brief. 2023 Jun 27;50:109355. (PMID: 37609648) |
| معلومات مُعتمدة: | BFP/RGP/ICT/22/003 TRC -MOHERI |
| فهرسة مساهمة: | Keywords: CNN; R-FCN; UAV; YOLO; bird strike; deep learning |
| تواريخ الأحداث: | Date Created: 20240914 Date Completed: 20240914 Latest Revision: 20240916 |
| رمز التحديث: | 20240916 |
| مُعرف محوري في PubMed: | PMC11398100 |
| DOI: | 10.3390/s24175455 |
| PMID: | 39275366 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1424-8220 |
|---|---|
| DOI: | 10.3390/s24175455 |