Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 12 Sayı: 1, 151 - 157, 22.03.2023
https://doi.org/10.17798/bitlisfen.1217727

Öz

Kaynakça

  • [1] International Civil Aviation Organization (ICAO) Annex 14 Recommendation.
  • [2] S. A.Yahyaai, A. A. Khan, M. A.Siyabi, A.Mehmood, T. Hussain, “LiDAR Based Remote Sensing System for Foreign Object Debris Detection ( FODD )”, Journal of Space Technology, 2020, Vol. 10(1) 13-18.
  • [3] https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_150_5220-24.pdf (accessed in 13.03.2023)
  • [4] H. Xu, Z. Han, S. Feng, H. Zhou and Y. Fang, “Foreign object debris material recognition based on convolutional neural networks”, EURASIP Journal on Image and Video Processing, 2018:21, https://doi.org/10.1186/s13640-018-0261-2
  • [5] P. Li, H. Li, “Research on FOD Detection for Airport Runway based on YOLOv3”, Proceedings of the 39th Chinese Control Conference, July 27-29, 2020, Shenyang, China
  • [6] T. Munyer, Pei-Chi Huang, C. Huang, X. Zhong, “FOD-A: A Dataset for Foreign Object Debris in Airports”, https://arxiv.org/abs/2110.03072
  • [7] T.J.E. Munyer, C. Huang, D. Brinkman, X. Zhong, “Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection”, Proc Int Conf Digit Gov Res. 2021 Jun;2021:437-443. doi: 10.1145/3463677.3463743. Epub 2021 Jun 9. PMID: 35098266; PMCID: PMC8796661.
  • [8] M. Noroozi, A. Shah, “Towards optimal foreign object debris detection in an airport environment”, Expert Systems with Applications, 2023, Vol.213, Part A, 118829, Issn 0957-4174, https://doi.org/10.1016/j.eswa.2022.118829.
  • [9] X. Cao, P. Wang, C. Meng, X Bai, G. Gong, M Liu, J Qi, “Region Based CNN for Foreign Object Debris Detection on Airfield Pavement”, Sensors 2018, 18, 737. https://doi.org/10.3390/s18030737.
  • [10] Y. Liu, Y. Li, J. Liu, X. Peng, Y. Zhou and Y. L. Murphey, "FOD Detection using DenseNet with Focal Loss of Object Samples for Airport Runway," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 547-554, doi: 10.1109/SSCI.2018.8628648.
  • [11] A. Parker, F. Gonzalez and P. Trotter, “Live Detection of Foreign Object Debris on Runways Detection using Drones and AI”, 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 2022, pp. 1-13, doi: 10.1109/AERO53065.2022.9843697.
  • [12] Y. Jing., H. Zheng, C. Lin, W. Zheng, K. Dong, and X. Li. "Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest", Sensors 22, no. 7: 2463. https://doi.org/10.3390/s22072463.
  • [13] D. R Shaker and A.R Abbas, “Foreign Object Debris Material Recognition based on Ensemble Learning Algorithm” J. Phys.: Conf. Ser. 2022, 2322 012091 DOI 10.1088/1742-6596/2322/1/012091
  • [14] https://drive.google.com/file/d/1UTxZQUipkX6_rC9AoCaweeeeOPrOG1_B (accessed in 13.03.2023)
  • [15] A. Addapa, P. Ramaswamy & K. Mungara, “Object Detection/Recognition Using Machine Learning Techniques in AWS”, 2020, The International journal of analytical and experimental modal analysis, ISSN NO:0886-9367.
  • [16] A. T. Ali, H. S. Abdullah & M.N. Fadhil, “Voice recognition system using machine learning techniques” Materials Today: 2021 Proceedings. ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.04.075
  • [17] M.M. Ahsan, S.A. Luna, Z. Siddique, “Machine-Learning-Based Disease Diagnosis: A Comprehensive Review”, Healthcare (Basel), 2022 Mar 15;10(3):541. doi: 10.3390/healthcare10030541. PMID: 35327018; PMCID: PMC8950225.
  • [18] M. Coşkun, Ö. Yıldırım., A. Uçar, Y. Demir. "An Overview of Popular Deep Learning Methods". European Journal of Technique (EJT) 7, 2017: 165-176
  • [19] A. Krizhevsky, I. Sutskever & G. E. Hinton, “Imagenet classification with deep convolutional neural networks". Advances in neural information processing systems, 25, 2012.
  • [20] Md. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, B. C V. Esesn, A A S. Awwal & V. K. Asari, “The history began from alexnet: A comprehensive survey on deep learning approaches”,2018, arXiv preprint arXiv:1803.01164.
  • [21] https://www.mathworks.com/help/deeplearning/ref/resnet18.html. (accessed in 18.01.2023)
  • [22] F.N. Iandola, S. Han, M.W. Moskewicz, K. Ashraf, W.J. Dally, K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size”, 3th International Conference on Learning Representations. Toulon: ICLR, 2016, pp.1-13, 2016
  • [23] F. Kurt "Evrişimli Sinir Ağlarında Hiper Parametrelerin Etkisinin İncelenmesi," Yüksek Lisans Tezi, Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara, 2018.
  • [24] E. Seyyarer, F. Ayata, T. Uçkan, & A. Karci, “Derin Öğrenmede Kullanılan Optimizasyon Algoritmalarının Uygulanması ve Kıyaslanması” Computer Science, 2020, 5 (2), 90-98.
  • [25] https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (accessed in 14.03.2023).

Examining The Effect of Different Networks on Foreign Object Debris Detection

Yıl 2023, Cilt: 12 Sayı: 1, 151 - 157, 22.03.2023
https://doi.org/10.17798/bitlisfen.1217727

Öz

Foreign Object Debris (FOD) at airports poses a risk to aircraft and passenger safety. FOD can seriously harm aircraft engines and injure personnel. Accurate and careful FOD detection is of great importance for a safe flight.
According to the FAA's report, FOD types are aircraft fasteners such as nut, safety; aircraft parts such as fuel blast, landing gear parts, rubber parts; construction materials such as wooden pieces, stones; plastic materials, natural plant and animal parts. For this purpose, in this study, the effect of different networks and optimizer on object detection and accuracy analysis were examined by using a data set of possible materials at the airport. AlexNet, Resnet18 and Squeezenet networks were used. Application is applied two stages. The first one, 3000 data were divided into two parts, 70% to 30%, training and test data, and the results were obtained. The second one, 3000 data were used for training, except for the training data, 440 data were used for validation. Also, for each application, both SGDM and ADAM optimizer are used. The best result is obtained from ADAM optimizer with Resnet18, accuracy rate is %99,56.

Kaynakça

  • [1] International Civil Aviation Organization (ICAO) Annex 14 Recommendation.
  • [2] S. A.Yahyaai, A. A. Khan, M. A.Siyabi, A.Mehmood, T. Hussain, “LiDAR Based Remote Sensing System for Foreign Object Debris Detection ( FODD )”, Journal of Space Technology, 2020, Vol. 10(1) 13-18.
  • [3] https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_150_5220-24.pdf (accessed in 13.03.2023)
  • [4] H. Xu, Z. Han, S. Feng, H. Zhou and Y. Fang, “Foreign object debris material recognition based on convolutional neural networks”, EURASIP Journal on Image and Video Processing, 2018:21, https://doi.org/10.1186/s13640-018-0261-2
  • [5] P. Li, H. Li, “Research on FOD Detection for Airport Runway based on YOLOv3”, Proceedings of the 39th Chinese Control Conference, July 27-29, 2020, Shenyang, China
  • [6] T. Munyer, Pei-Chi Huang, C. Huang, X. Zhong, “FOD-A: A Dataset for Foreign Object Debris in Airports”, https://arxiv.org/abs/2110.03072
  • [7] T.J.E. Munyer, C. Huang, D. Brinkman, X. Zhong, “Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection”, Proc Int Conf Digit Gov Res. 2021 Jun;2021:437-443. doi: 10.1145/3463677.3463743. Epub 2021 Jun 9. PMID: 35098266; PMCID: PMC8796661.
  • [8] M. Noroozi, A. Shah, “Towards optimal foreign object debris detection in an airport environment”, Expert Systems with Applications, 2023, Vol.213, Part A, 118829, Issn 0957-4174, https://doi.org/10.1016/j.eswa.2022.118829.
  • [9] X. Cao, P. Wang, C. Meng, X Bai, G. Gong, M Liu, J Qi, “Region Based CNN for Foreign Object Debris Detection on Airfield Pavement”, Sensors 2018, 18, 737. https://doi.org/10.3390/s18030737.
  • [10] Y. Liu, Y. Li, J. Liu, X. Peng, Y. Zhou and Y. L. Murphey, "FOD Detection using DenseNet with Focal Loss of Object Samples for Airport Runway," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 547-554, doi: 10.1109/SSCI.2018.8628648.
  • [11] A. Parker, F. Gonzalez and P. Trotter, “Live Detection of Foreign Object Debris on Runways Detection using Drones and AI”, 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 2022, pp. 1-13, doi: 10.1109/AERO53065.2022.9843697.
  • [12] Y. Jing., H. Zheng, C. Lin, W. Zheng, K. Dong, and X. Li. "Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest", Sensors 22, no. 7: 2463. https://doi.org/10.3390/s22072463.
  • [13] D. R Shaker and A.R Abbas, “Foreign Object Debris Material Recognition based on Ensemble Learning Algorithm” J. Phys.: Conf. Ser. 2022, 2322 012091 DOI 10.1088/1742-6596/2322/1/012091
  • [14] https://drive.google.com/file/d/1UTxZQUipkX6_rC9AoCaweeeeOPrOG1_B (accessed in 13.03.2023)
  • [15] A. Addapa, P. Ramaswamy & K. Mungara, “Object Detection/Recognition Using Machine Learning Techniques in AWS”, 2020, The International journal of analytical and experimental modal analysis, ISSN NO:0886-9367.
  • [16] A. T. Ali, H. S. Abdullah & M.N. Fadhil, “Voice recognition system using machine learning techniques” Materials Today: 2021 Proceedings. ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.04.075
  • [17] M.M. Ahsan, S.A. Luna, Z. Siddique, “Machine-Learning-Based Disease Diagnosis: A Comprehensive Review”, Healthcare (Basel), 2022 Mar 15;10(3):541. doi: 10.3390/healthcare10030541. PMID: 35327018; PMCID: PMC8950225.
  • [18] M. Coşkun, Ö. Yıldırım., A. Uçar, Y. Demir. "An Overview of Popular Deep Learning Methods". European Journal of Technique (EJT) 7, 2017: 165-176
  • [19] A. Krizhevsky, I. Sutskever & G. E. Hinton, “Imagenet classification with deep convolutional neural networks". Advances in neural information processing systems, 25, 2012.
  • [20] Md. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, B. C V. Esesn, A A S. Awwal & V. K. Asari, “The history began from alexnet: A comprehensive survey on deep learning approaches”,2018, arXiv preprint arXiv:1803.01164.
  • [21] https://www.mathworks.com/help/deeplearning/ref/resnet18.html. (accessed in 18.01.2023)
  • [22] F.N. Iandola, S. Han, M.W. Moskewicz, K. Ashraf, W.J. Dally, K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size”, 3th International Conference on Learning Representations. Toulon: ICLR, 2016, pp.1-13, 2016
  • [23] F. Kurt "Evrişimli Sinir Ağlarında Hiper Parametrelerin Etkisinin İncelenmesi," Yüksek Lisans Tezi, Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara, 2018.
  • [24] E. Seyyarer, F. Ayata, T. Uçkan, & A. Karci, “Derin Öğrenmede Kullanılan Optimizasyon Algoritmalarının Uygulanması ve Kıyaslanması” Computer Science, 2020, 5 (2), 90-98.
  • [25] https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (accessed in 14.03.2023).
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Duygu Kaya 0000-0002-6453-631X

Erken Görünüm Tarihi 23 Mart 2023
Yayımlanma Tarihi 22 Mart 2023
Gönderilme Tarihi 12 Aralık 2022
Kabul Tarihi 23 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 1

Kaynak Göster

IEEE D. Kaya, “Examining The Effect of Different Networks on Foreign Object Debris Detection”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 12, sy. 1, ss. 151–157, 2023, doi: 10.17798/bitlisfen.1217727.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr