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MEKANSAL PİRAMİT HAVUZLAMA TABANLI EVRİŞİMLİ SİNİR AĞI İLE OTOMATİK DRONE SINIFLANDIRMA

Year 2022, Volume: 25 Issue: 3, 329 - 340, 03.09.2022

Deniz Korkmaz , Hakan Açıkgöz

https://doi.org/10.17780/ksujes.1113669
Cited By: 1

Abstract

Hava sahalarının önemli olduğu bölgelerde dronları tespit etmek zorlu bir konu haline gelmiştir. Bu araçların kontrolsüz uçuşları ve konuşlanmaları da istenmeyen bölgelerde çeşitli güvenlik sorunlarına sebep olur. Bu çalışmada, dronları kuşlardan ayırarak etkili bir şekilde sınıflandırabilmek için bir evrişimli sinir ağı (ESA) modeli önerilmiştir. Önerilen model, ön eğitimli AlexNet ile mekansal piramit havuzlama (MPH) yapısı kullanılarak tasarlanmıştır. Böylece, ağın evrişimsel katmanlarından gelen yerel öznitelikler birleştirerek ağın nesne özelliklerini daha kapsamlı bir şekilde öğrenmesi sağlanmış ve önerilen modelin sınıflandırma performansı artırılmıştır. Ayrıca, eğitim görüntülerinde çevrimdışı veri artırma tekniği uygulanarak örnek sayısı artırılmıştır. Önerilen yöntemin performansı AlexNet, ShuffleNet, GoogLeNet ve DarkNet gibi sıklıkla kullanılan ön eğitimli ESA mimarileri ile karşılaştırılmıştır. Gerçekleştirilen deneysel çalışmalarda önerilen yöntemin doğruluk, kesinlik, duyarlılık, özgüllük ve F1-skor değerleri sırasıyla %98.89, %97.83, %100, %97.78 ve %98.90 olarak elde edilmiştir. Çalışmada elde edilen tüm sonuçlar incelendiğinde, önerilen yöntemin farklı ortamlara ait drone görüntülerini kuşlardan ayırarak başarımı yüksek bir şekilde sınıflayabildiğini ortaya koymaktadır.

Keywords

Drone sınıflandırma evrişimli sinir ağları mekansal piramit hazulama derin öğrenme

References

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  • Basak, S., Rajendran, S., Pollin, S., Scheers, B., 2022. Combined RF-Based Drone Detection and Classification. IEEE Trans. Cogn. Commun. Netw. 8, 111–120. https://doi.org/10.1109/TCCN.2021.3099114
  • Basbug, A.M., Sert, M., 2019. Acoustic Scene Classification Using Spatial Pyramid Pooling with Convolutional Neural Networks. 13th IEEE Int. Conf. Semant. Comput. ICSC 2019 128–131. https://doi.org/10.1109/ICSC.2019.00029
  • Coluccia, A., Fascista, A., Schumann, A., Sommer, L., Dimou, A., Zarpalas, D., Méndez, M., de la Iglesia, D., González, I., Mercier, J.-P., Gagné, G., Mitra, A., Rajashekar, S., 2021. Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge. Sensors 21, 2824. https://doi.org/10.3390/s21082824
  • Coluccia, A., Parisi, G., Fascista, A., 2020. Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors 20, 4172. https://doi.org/10.3390/s20154172
  • Dale, H., Baker, C., Antoniou, M., Jahangir, M., Atkinson, G., Harman, S., 2022. SNR‐dependent drone classification using convolutional neural networks. IET Radar, Sonar Navig. 16, 22–33. https://doi.org/10.1049/rsn2.12161
  • Grác, Š., Beňo, P., Duchoň, F., Dekan, M., Tölgyessy, M., 2020. Automated detection of multi-rotor UAVs using a machine-learning approach. Appl. Syst. Innov. 3, 1–23. https://doi.org/10.3390/asi3030029
  • Han, X., Zhong, Y., Cao, L., Zhang, L., 2017. Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens. 9. https://doi.org/10.3390/rs9080848
  • Hassanalian, M., Abdelkefi, A., 2017. Classifications, applications, and design challenges of drones: A review. Prog. Aerosp. Sci. 91, 99–131. https://doi.org/10.1016/j.paerosci.2017.04.003
  • Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., Wang, R., 2020. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Inf. Sci. (Ny). 522, 241–258. https://doi.org/10.1016/j.ins.2020.02.067
  • Jamil, S., n.d. Malicious Drones Dataset [WWW Document]. URL https://www.kaggle.com/datasets/sonainjamil/malicious-drones
  • Kim, B.K., Kang, H.S., Lee, S., Park, S.O., 2021. Improved Drone Classification Using Polarimetric Merged-Doppler Images. IEEE Geosci. Remote Sens. Lett. 18, 1946–1950. https://doi.org/10.1109/LGRS.2020.3011114
  • Kılıç, R., Kumbasar, N., Oral, E.A., Ozbek, I.Y., 2022. Drone classification using RF signal based spectral features. Eng. Sci. Technol. an Int. J. 28, 101028. https://doi.org/10.1016/j.jestch.2021.06.008
  • Lashgari, E., Liang, D., Maoz, U., 2020. Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885. https://doi.org/10.1016/j.jneumeth.2020.108885
  • Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J., 2021. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Networks Learn. Syst. 1–21. https://doi.org/10.1109/tnnls.2021.3084827
  • Liu, J., Xu, Q.Y., Chen, W.S., 2021. Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data. IEEE Access 9, 160135–160144. https://doi.org/10.1109/ACCESS.2021.3130231
  • Lykou, G., Moustakas, D., Gritzalis, D., 2020. Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies. Sensors 20, 3537. https://doi.org/10.3390/s20123537
  • Oh, H.M., Lee, H., Kim, M.Y., 2019. Comparing Convolutional Neural Network(CNN) models for machine learning-based drone and bird classification of anti-drone system. Int. Conf. Control. Autom. Syst. 2019-Octob, 87–90. https://doi.org/10.23919/ICCAS47443.2019.8971699
  • Ouyang, X., Gu, K., Zhou, P., 2018. Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification. IEEE/ACM Trans. Audio Speech Lang. Process. 26, 2167–2179. https://doi.org/10.1109/TASLP.2018.2852502
  • Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., Matson, E.T., 2020. Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors 20, 3856. https://doi.org/10.3390/s20143856
  • Singha, S., Aydin, B., 2021. Automated drone detection using YOLOv4. Drones 5. https://doi.org/10.3390/drones5030095
  • Taha, B., Shoufan, A., 2019. Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research. IEEE Access 7, 138669–138682. https://doi.org/10.1109/ACCESS.2019.2942944
  • Takahashi, R., Matsubara, T., Uehara, K., 2020. Data Augmentation Using Random Image Cropping and Patching for Deep CNNs. IEEE Trans. Circuits Syst. Video Technol. 30, 2917–2931. https://doi.org/10.1109/TCSVT.2019.2935128
  • Tan, Y.S., Lim, K.M., Tee, C., Lee, C.P., Low, C.Y., 2021. Convolutional neural network with spatial pyramid pooling for hand gesture recognition. Neural Comput. Appl. 33, 5339–5351. https://doi.org/10.1007/s00521-020-05337-0
  • Uddin, Z., Altaf, M., Bilal, M., Nkenyereye, L., Bashir, A.K., 2020. Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference. Comput. Commun. 154, 236–245. https://doi.org/10.1016/j.comcom.2020.02.065
  • Walia, H., n.d. Bird vs Drone New Dataset [WWW Document]. URL https://www.kaggle.com/datasets/harshwalia/bird-vs-drone-new
  • Wojtanowski, J., Zygmunt, M., Drozd, T., Jakubaszek, M., Życzkowski, M., Muzal, M., 2021. Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement. Sensors 21, 5597. https://doi.org/10.3390/s21165597
  • Yee, P.S., Lim, K.M., Lee, C.P., 2022. DeepScene: Scene classification via convolutional neural network with spatial pyramid pooling. Expert Syst. Appl. 193, 116382. https://doi.org/10.1016/j.eswa.2021.116382
  • YEŞİLAY, R.B., MACİT, A., 2020. DÜNYADA VE TÜRKİYE’DE DRONE EKONOMİSİ: GELECEĞE YÖNELİK BEKLENTİLER. Beykoz Akad. Derg. 8, 239–251. https://doi.org/10.14514/BYK.m.26515393.2020.8/1.239-251
  • Yue, J., Mao, S., Li, M., 2016. A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sens. Lett. 7, 875–884. https://doi.org/10.1080/2150704X.2016.1193793
  • Zhang, X., Mehta, V., Bolic, M., Mantegh, I., 2020. Hybrid AI-enabled Method for UAS and Bird Detection and Classification. IEEE Int. Conf. Syst. Man Cybern. 2020-Octob, 2803–2807. https://doi.org/10.1109/SMC42975.2020.9282965

AUTOMATED DRONE CLASSIFICATION WITH A SPATIAL PYRAMID POOLING-BASED CONVENTIONAL NEURAL NETWORK

Year 2022, Volume: 25 Issue: 3, 329 - 340, 03.09.2022

Deniz Korkmaz , Hakan Açıkgöz

https://doi.org/10.17780/ksujes.1113669
Cited By: 1

Abstract

Keywords

Drone classification convolutional neural networks spatial pyramid pooling deep learning

References

  • Anwar, M.Z., Kaleem, Z., Jamalipour, A., 2019. Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications. IEEE Trans. Veh. Technol. 68, 2526–2534. https://doi.org/10.1109/TVT.2019.2893615
  • Basak, S., Rajendran, S., Pollin, S., Scheers, B., 2022. Combined RF-Based Drone Detection and Classification. IEEE Trans. Cogn. Commun. Netw. 8, 111–120. https://doi.org/10.1109/TCCN.2021.3099114
  • Basbug, A.M., Sert, M., 2019. Acoustic Scene Classification Using Spatial Pyramid Pooling with Convolutional Neural Networks. 13th IEEE Int. Conf. Semant. Comput. ICSC 2019 128–131. https://doi.org/10.1109/ICSC.2019.00029
  • Coluccia, A., Fascista, A., Schumann, A., Sommer, L., Dimou, A., Zarpalas, D., Méndez, M., de la Iglesia, D., González, I., Mercier, J.-P., Gagné, G., Mitra, A., Rajashekar, S., 2021. Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge. Sensors 21, 2824. https://doi.org/10.3390/s21082824
  • Coluccia, A., Parisi, G., Fascista, A., 2020. Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors 20, 4172. https://doi.org/10.3390/s20154172
  • Dale, H., Baker, C., Antoniou, M., Jahangir, M., Atkinson, G., Harman, S., 2022. SNR‐dependent drone classification using convolutional neural networks. IET Radar, Sonar Navig. 16, 22–33. https://doi.org/10.1049/rsn2.12161
  • Grác, Š., Beňo, P., Duchoň, F., Dekan, M., Tölgyessy, M., 2020. Automated detection of multi-rotor UAVs using a machine-learning approach. Appl. Syst. Innov. 3, 1–23. https://doi.org/10.3390/asi3030029
  • Han, X., Zhong, Y., Cao, L., Zhang, L., 2017. Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens. 9. https://doi.org/10.3390/rs9080848
  • Hassanalian, M., Abdelkefi, A., 2017. Classifications, applications, and design challenges of drones: A review. Prog. Aerosp. Sci. 91, 99–131. https://doi.org/10.1016/j.paerosci.2017.04.003
  • Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., Wang, R., 2020. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Inf. Sci. (Ny). 522, 241–258. https://doi.org/10.1016/j.ins.2020.02.067
  • Jamil, S., n.d. Malicious Drones Dataset [WWW Document]. URL https://www.kaggle.com/datasets/sonainjamil/malicious-drones
  • Kim, B.K., Kang, H.S., Lee, S., Park, S.O., 2021. Improved Drone Classification Using Polarimetric Merged-Doppler Images. IEEE Geosci. Remote Sens. Lett. 18, 1946–1950. https://doi.org/10.1109/LGRS.2020.3011114
  • Kılıç, R., Kumbasar, N., Oral, E.A., Ozbek, I.Y., 2022. Drone classification using RF signal based spectral features. Eng. Sci. Technol. an Int. J. 28, 101028. https://doi.org/10.1016/j.jestch.2021.06.008
  • Lashgari, E., Liang, D., Maoz, U., 2020. Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885. https://doi.org/10.1016/j.jneumeth.2020.108885
  • Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J., 2021. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Networks Learn. Syst. 1–21. https://doi.org/10.1109/tnnls.2021.3084827
  • Liu, J., Xu, Q.Y., Chen, W.S., 2021. Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data. IEEE Access 9, 160135–160144. https://doi.org/10.1109/ACCESS.2021.3130231
  • Lykou, G., Moustakas, D., Gritzalis, D., 2020. Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies. Sensors 20, 3537. https://doi.org/10.3390/s20123537
  • Oh, H.M., Lee, H., Kim, M.Y., 2019. Comparing Convolutional Neural Network(CNN) models for machine learning-based drone and bird classification of anti-drone system. Int. Conf. Control. Autom. Syst. 2019-Octob, 87–90. https://doi.org/10.23919/ICCAS47443.2019.8971699
  • Ouyang, X., Gu, K., Zhou, P., 2018. Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification. IEEE/ACM Trans. Audio Speech Lang. Process. 26, 2167–2179. https://doi.org/10.1109/TASLP.2018.2852502
  • Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., Matson, E.T., 2020. Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors 20, 3856. https://doi.org/10.3390/s20143856
  • Singha, S., Aydin, B., 2021. Automated drone detection using YOLOv4. Drones 5. https://doi.org/10.3390/drones5030095
  • Taha, B., Shoufan, A., 2019. Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research. IEEE Access 7, 138669–138682. https://doi.org/10.1109/ACCESS.2019.2942944
  • Takahashi, R., Matsubara, T., Uehara, K., 2020. Data Augmentation Using Random Image Cropping and Patching for Deep CNNs. IEEE Trans. Circuits Syst. Video Technol. 30, 2917–2931. https://doi.org/10.1109/TCSVT.2019.2935128
  • Tan, Y.S., Lim, K.M., Tee, C., Lee, C.P., Low, C.Y., 2021. Convolutional neural network with spatial pyramid pooling for hand gesture recognition. Neural Comput. Appl. 33, 5339–5351. https://doi.org/10.1007/s00521-020-05337-0
  • Uddin, Z., Altaf, M., Bilal, M., Nkenyereye, L., Bashir, A.K., 2020. Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference. Comput. Commun. 154, 236–245. https://doi.org/10.1016/j.comcom.2020.02.065
  • Walia, H., n.d. Bird vs Drone New Dataset [WWW Document]. URL https://www.kaggle.com/datasets/harshwalia/bird-vs-drone-new
  • Wojtanowski, J., Zygmunt, M., Drozd, T., Jakubaszek, M., Życzkowski, M., Muzal, M., 2021. Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement. Sensors 21, 5597. https://doi.org/10.3390/s21165597
  • Yee, P.S., Lim, K.M., Lee, C.P., 2022. DeepScene: Scene classification via convolutional neural network with spatial pyramid pooling. Expert Syst. Appl. 193, 116382. https://doi.org/10.1016/j.eswa.2021.116382
  • YEŞİLAY, R.B., MACİT, A., 2020. DÜNYADA VE TÜRKİYE’DE DRONE EKONOMİSİ: GELECEĞE YÖNELİK BEKLENTİLER. Beykoz Akad. Derg. 8, 239–251. https://doi.org/10.14514/BYK.m.26515393.2020.8/1.239-251
  • Yue, J., Mao, S., Li, M., 2016. A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sens. Lett. 7, 875–884. https://doi.org/10.1080/2150704X.2016.1193793
  • Zhang, X., Mehta, V., Bolic, M., Mantegh, I., 2020. Hybrid AI-enabled Method for UAS and Bird Detection and Classification. IEEE Int. Conf. Syst. Man Cybern. 2020-Octob, 2803–2807. https://doi.org/10.1109/SMC42975.2020.9282965
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Computer Engineering
Authors

Deniz Korkmaz 0000-0002-5159-0659

Hakan Açıkgöz 0000-0002-6432-7243

Publication Date September 3, 2022
Submission Date May 7, 2022
Published in Issue Year 2022Volume: 25 Issue: 3

Cite

APA Korkmaz, D., & Açıkgöz, H. (2022). MEKANSAL PİRAMİT HAVUZLAMA TABANLI EVRİŞİMLİ SİNİR AĞI İLE OTOMATİK DRONE SINIFLANDIRMA. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(3), 329-340. https://doi.org/10.17780/ksujes.1113669

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