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FINE-GRAINED CLASSIFICATION OF MILITARY AIRCRAFT USING PRE-TRAINED DEEP LEARNING MODELS AND YOLO11

Yıl 2024, Cilt: 2 Sayı: 2, 150 - 171, 17.01.2025

Hasan Karaca Nesrin Aydın Atasoy

https://doi.org/10.71074/CTC.1578917

Öz

This research examines the potential of pre-trained deep learning models for the fine-grained classification of military aircraft, to achieve accurate identification and extraction of unique tail numbers. The study uses a publicly available dataset comprising 43 classes of military aircraft, with a total of 24,164 images for training and 6,042 images for testing. The performance of five distinct pre-trained convolutional neural network (CNN) architectures, including DenseNet121, MobileNetV2, ResNet50, ResNet101, and VGG19, is evaluated and compared. Furthermore, the paper examines the effectiveness of the YOLO11 model family for aircraft classification, particularly emphasizing the YOLO11x-cls model’s superior performance. The study analyses the training results and confusion matrix of the YOLO11x-cls model, demonstrating its accuracy and ability to generalize well to unseen data. This work contributes to the advancement of AI-powered image recognition for military aviation applications, potentially improving data collection, monitoring, and analysis processes.

Anahtar Kelimeler

Fine-Grained Classification Deep Learning Convolutional Neural Networks YOLO11 ResNet50

Kaynakça

  • Mori, S., H. Nishida, and H. Yamada, Optical character recognition. 1999: John Wiley & Sons, Inc.
  • Mekonnen, I., Automated Aircraft Identification by Machine Vision. 2017.
  • Tomovic, S., K. Pavlovic, and M. Bajceta, Aligning document layouts extracted with different OCR engines with clustering approach. Egyptian Informatics Journal, 2021. 22(3): p. 329-338.
  • Kobayashi, Y., et al., Basic research on a handwritten note image recognition system that combines two OCRs. Procedia Computer Science, 2021. 192: p. 2596-2605.
  • Zeng, G., et al., Beyond OCR + VQA: Towards end-to-end reading and reasoning for robust and accurate textvqa. Pattern Recognition, 2023. 138: p. 109337.
  • Onim, M.S.H., et al., BLPnet: A new DNN model and Bengali OCR engine for Automatic Licence Plate Recognition. Array, 2022. 15: p. 100244.
  • Lv, G., et al., COME: Clip-OCR and Master ObjEct for text image captioning. Image and Vision Computing, 2023. 136: p. 104751.
  • Imam, N.H., V.G. Vassilakis, and D. Kolovos, OCR post-correction for detecting adversarial text images. Journal of Information Security and Applications, 2022. 66: p. 103170.
  • Irimia, C., et al., Official Document Identification and Data Extraction using Templates and OCR. Procedia Computer Science, 2022. 207: p. 1571-1580.
  • Dutta, H. and A. Gupta, PNRank: Unsupervised ranking of person name entities from noisy OCR text. Decision Support Systems, 2022. 152: p. 113662.
  • Oucheikh, R., T. Pettersson, and T. Lo¨fstro¨m, Product verification using OCR classification and Mondrian conformal prediction. Expert Systems with Applications, 2022. 188: p. 115942.
  • Mei, J., et al., Statistical learning for OCR error correction. Information Processing & Management, 2018. 54(6): p. 874-887.
  • Shen, Z., et al. Deep learning based framework for automatic damage detection in aircraft engine borescope inspection. in 2019 International Conference on Computing, Networking and Communications (ICNC). 2019. IEEE.
  • Sun, X., et al., SCAN: Scattering characteristics analysis network for few-shot aircraft classification in high resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 60: p. 1-17.
  • Kiyak, E. and G. Unal, Small aircraft detection using deep learning. Aircraft Engineering and Aerospace Technology, 2021. 93(4): p. 671-681.
  • Khan, S.N., et al. Rapid Aircraft Classification in Satellite Imagery using Fully Convolutional Residual Network. in 2020 International Conference on Emerging Trends in Smart Technologies (ICETST). 2020. IEEE.
  • Kang, Y., et al., ST-Net: Scattering Topology Network for Aircraft Classification in High-Resolution SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 2023. 61: p. 1-17.
  • Hassan, A., et al. A deep learning framework for automatic airplane detection in remote sensing satellite images. in 2019 IEEE Aerospace Conference. 2019. IEEE.
  • Dolph, C., et al. Aircraft Classification Using RADAR from small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations. in AIAA AVIATION 2021 FORUM. 2021.
  • Chen, Z., T. Zhang, and C. Ouyang, End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing, 2018. 10(1): p. 139.
  • Azam, F., et al., Aircraft classification based on PCA and feature fusion techniques in convolutional neural network. IEEE Access, 2021. 9: p. 161683-161694.
  • Alshaibani, W., et al., Airplane Type Identification Based on Mask RCNN and Drone Images. arXiv preprint arXiv:2108.12811, 2021.
  • Alganci, U., M. Soydas, and E. Sertel, Comparative research on deep learning approaches for airplane detection from very high-resolution satellite images. Remote sensing, 2020. 12(3): p. 458.
  • LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444.
  • Gao, Z., & Yi, W. (2025). Optimizing projectile aerodynamic parameter identification of kernel extreme learning ma- chine based on improved Dung Beetle Optimizer algorithm. Measurement, 239, 115473.
  • Song, T., Nguyen, L. T. H., & Ta, T. V. (2025). MPSA-DenseNet: A novel deep learning model for English accent classification. Computer Speech & Language, 89, 101676.
  • Zhang, Y., Liu, R., Wang, X., Chen, H., & Li, C. (2021). Boosted binary Harris hawks optimizer and feature selection. Engineering with Computers, 37, 3741-3770.
  • Prakash, N. N., Rajesh, V., Namakhwa, D. L., Pande, S. D., & Ahammad, S. H. (2023). A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis. Scientific African, 20, e01629.
  • Data Statement Dataset is available at https://www.kaggle.com/datasets/a2015003713/ militaryaircraftdetectiondataset
  • Azam, F., Rizvi, A., Khan, W. Z., Aalsalem, M. Y., Yu, H., Zikria, Y. B. (2021). Aircraft classification based on PCA and feature fusion techniques in convolutional neural network. IEEE Access, 9, 161683-161694.
  • Barbarosou, M., Paraskevas, I., Ahmed, A. (2016). Military aircrafts’ classification based on their sound signature. Aircraft Engineering and Aerospace Technology: An International Journal, 88(1), 66-72.
  • Karacor, A. G., Torun, E., Abay, R. (2011). Aircraft classification using image processing techniques and artificial neural networks. International Journal of Pattern Recognition and Artificial Intelligence, 25(08), 1321-1335.
  • Luo, S., Yu, J., Xi, Y., Liao, X. (2022). Aircraft target detection in remote sensing images based on improved YOLOv5. IEEE Access, 10, 5184-5192.
  • Fine-Grained Visual Classification of Aircraft, S. Maji, J. Kannala, E. Rahtu, M. Blaschko, A. Vedaldi, arXiv.org, 2013
  • https://www.airplanes-online.com/

Yıl 2024, Cilt: 2 Sayı: 2, 150 - 171, 17.01.2025

Hasan Karaca Nesrin Aydın Atasoy

https://doi.org/10.71074/CTC.1578917

Öz

Kaynakça

  • Mori, S., H. Nishida, and H. Yamada, Optical character recognition. 1999: John Wiley & Sons, Inc.
  • Mekonnen, I., Automated Aircraft Identification by Machine Vision. 2017.
  • Tomovic, S., K. Pavlovic, and M. Bajceta, Aligning document layouts extracted with different OCR engines with clustering approach. Egyptian Informatics Journal, 2021. 22(3): p. 329-338.
  • Kobayashi, Y., et al., Basic research on a handwritten note image recognition system that combines two OCRs. Procedia Computer Science, 2021. 192: p. 2596-2605.
  • Zeng, G., et al., Beyond OCR + VQA: Towards end-to-end reading and reasoning for robust and accurate textvqa. Pattern Recognition, 2023. 138: p. 109337.
  • Onim, M.S.H., et al., BLPnet: A new DNN model and Bengali OCR engine for Automatic Licence Plate Recognition. Array, 2022. 15: p. 100244.
  • Lv, G., et al., COME: Clip-OCR and Master ObjEct for text image captioning. Image and Vision Computing, 2023. 136: p. 104751.
  • Imam, N.H., V.G. Vassilakis, and D. Kolovos, OCR post-correction for detecting adversarial text images. Journal of Information Security and Applications, 2022. 66: p. 103170.
  • Irimia, C., et al., Official Document Identification and Data Extraction using Templates and OCR. Procedia Computer Science, 2022. 207: p. 1571-1580.
  • Dutta, H. and A. Gupta, PNRank: Unsupervised ranking of person name entities from noisy OCR text. Decision Support Systems, 2022. 152: p. 113662.
  • Oucheikh, R., T. Pettersson, and T. Lo¨fstro¨m, Product verification using OCR classification and Mondrian conformal prediction. Expert Systems with Applications, 2022. 188: p. 115942.
  • Mei, J., et al., Statistical learning for OCR error correction. Information Processing & Management, 2018. 54(6): p. 874-887.
  • Shen, Z., et al. Deep learning based framework for automatic damage detection in aircraft engine borescope inspection. in 2019 International Conference on Computing, Networking and Communications (ICNC). 2019. IEEE.
  • Sun, X., et al., SCAN: Scattering characteristics analysis network for few-shot aircraft classification in high resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 60: p. 1-17.
  • Kiyak, E. and G. Unal, Small aircraft detection using deep learning. Aircraft Engineering and Aerospace Technology, 2021. 93(4): p. 671-681.
  • Khan, S.N., et al. Rapid Aircraft Classification in Satellite Imagery using Fully Convolutional Residual Network. in 2020 International Conference on Emerging Trends in Smart Technologies (ICETST). 2020. IEEE.
  • Kang, Y., et al., ST-Net: Scattering Topology Network for Aircraft Classification in High-Resolution SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 2023. 61: p. 1-17.
  • Hassan, A., et al. A deep learning framework for automatic airplane detection in remote sensing satellite images. in 2019 IEEE Aerospace Conference. 2019. IEEE.
  • Dolph, C., et al. Aircraft Classification Using RADAR from small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations. in AIAA AVIATION 2021 FORUM. 2021.
  • Chen, Z., T. Zhang, and C. Ouyang, End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing, 2018. 10(1): p. 139.
  • Azam, F., et al., Aircraft classification based on PCA and feature fusion techniques in convolutional neural network. IEEE Access, 2021. 9: p. 161683-161694.
  • Alshaibani, W., et al., Airplane Type Identification Based on Mask RCNN and Drone Images. arXiv preprint arXiv:2108.12811, 2021.
  • Alganci, U., M. Soydas, and E. Sertel, Comparative research on deep learning approaches for airplane detection from very high-resolution satellite images. Remote sensing, 2020. 12(3): p. 458.
  • LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444.
  • Gao, Z., & Yi, W. (2025). Optimizing projectile aerodynamic parameter identification of kernel extreme learning ma- chine based on improved Dung Beetle Optimizer algorithm. Measurement, 239, 115473.
  • Song, T., Nguyen, L. T. H., & Ta, T. V. (2025). MPSA-DenseNet: A novel deep learning model for English accent classification. Computer Speech & Language, 89, 101676.
  • Zhang, Y., Liu, R., Wang, X., Chen, H., & Li, C. (2021). Boosted binary Harris hawks optimizer and feature selection. Engineering with Computers, 37, 3741-3770.
  • Prakash, N. N., Rajesh, V., Namakhwa, D. L., Pande, S. D., & Ahammad, S. H. (2023). A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis. Scientific African, 20, e01629.
  • Data Statement Dataset is available at https://www.kaggle.com/datasets/a2015003713/ militaryaircraftdetectiondataset
  • Azam, F., Rizvi, A., Khan, W. Z., Aalsalem, M. Y., Yu, H., Zikria, Y. B. (2021). Aircraft classification based on PCA and feature fusion techniques in convolutional neural network. IEEE Access, 9, 161683-161694.
  • Barbarosou, M., Paraskevas, I., Ahmed, A. (2016). Military aircrafts’ classification based on their sound signature. Aircraft Engineering and Aerospace Technology: An International Journal, 88(1), 66-72.
  • Karacor, A. G., Torun, E., Abay, R. (2011). Aircraft classification using image processing techniques and artificial neural networks. International Journal of Pattern Recognition and Artificial Intelligence, 25(08), 1321-1335.
  • Luo, S., Yu, J., Xi, Y., Liao, X. (2022). Aircraft target detection in remote sensing images based on improved YOLOv5. IEEE Access, 10, 5184-5192.
  • Fine-Grained Visual Classification of Aircraft, S. Maji, J. Kannala, E. Rahtu, M. Blaschko, A. Vedaldi, arXiv.org, 2013
  • https://www.airplanes-online.com/
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Research Article
Yazarlar

Hasan Karaca 0000-0002-8101-3860

Nesrin Aydın Atasoy 0000-0002-7188-0020

Erken Görünüm Tarihi 16 Ocak 2025
Yayımlanma Tarihi 17 Ocak 2025
Gönderilme Tarihi 4 Kasım 2024
Kabul Tarihi 13 Ocak 2025
Yayımlandığı Sayı Yıl 2024 Cilt: 2 Sayı: 2

Kaynak Göster

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