Skip to main content
SpringerLink
Log in

Research on Low Altitude Object Detection Based on Deep Convolution Neural Network

  • Conference paper
  • First Online:
Urban Intelligence and Applications

Part of the book series: Studies in Distributed Intelligence ((SDI))

  • 286 Accesses

Abstract

The rapid and accurate detection of low altitude objects means a great deal to flight safety in low altitude airspace; however, low altitude object detection is very challenging due to the images’ characteristics such as scale variations, arbitrary orientations, extremely large aspect ratio, and so on. In recent years, deep learning methods, which have demonstrated remarkable success for supervised learning tasks, are widely applied to the field of computer vision and good results have been achieved. Therefore, the deep learning method is applied to low altitude object detection in this paper. We proposed a deep convolution neural network model, which utilizes deep supervision implicitly through the dense layer-wise connections and combines multi-level and multi-scale feature. The model has achieved state-of-the-art performance on two large-scale publicly available datasets for object detection in aerial images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. D. Feng, A review on visualization of three-dimensional aerial corridor for low altitude safety. Electron. Meas. Technol. 41(9), 2–9 (2018)

    Google Scholar 

  2. G.S. Xia, X. Bai, J. Ding et al., DOTA: a large-scale dataset for object detection in aerial images. arXiv, pp. 1–17

    Google Scholar 

  3. G. Cheng, P. Zhou, J. Han, RIFD-CNN: rotation-invariant and fisher discriminative convolutional neural networks for object detection. in IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2016, pp. 2884–2893

    Google Scholar 

  4. Y. Long, Y. Gong, Z. Xiao, et al., Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(5), 2486–2498 (2017)

    Article  Google Scholar 

  5. G. Wang, X. Wang, B. Fan, et al., Feature extraction by rotation-invariant matrix representation for object detection in aerial image. IEEE Geosci. Remote Sens. Lett. 14(6), 851–855 (2017)

    Article  MathSciNet  Google Scholar 

  6. F. Zhang, B. Du, L. Zhang, et al., Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Trans. Geosci. Remote Sens. 54(9), 5553–5563 (2016)

    Article  Google Scholar 

  7. J. Deng, W. Dong, R. Socher et al., ImageNet: a large-scale hierarchical image database. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248–255

    Google Scholar 

  8. T. Lin, M. Maire, S. Belongie et al., Microsoft COCO: common objects in context. in European Conference on Computer Vision (ECCV), 2014, pp. 740–755

    Google Scholar 

  9. G. Huang, Z. Liu, K.Q. Weinberger, Densely connected convolutional networks. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269

    Google Scholar 

  10. X. Yuan, D. Li, D. Mohapatra, M. Elhoseny, Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Comput. Electr. Eng. 70, 813–825 (2018)

    Article  Google Scholar 

  11. B.S. Murugan, M. Elhoseny, K. Shankar, J. Uthayakumar, Region-based scalable smart system for anomaly detection in pedestrian walkways. Comput. Electr. Eng. 75, 146–160 (2019)

    Article  Google Scholar 

  12. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. in International Conference on Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105

    Google Scholar 

  13. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. in International Conference on Learning Representations (ICLR), 2015, pp. 1–13

    Google Scholar 

  14. C. Szegedy, W. Liu, Y. Jia et al., Going deeper with convolutions. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1–9

    Google Scholar 

  15. K. He, X. Zhang, S. Ren et al., Deep residual learning for image recognition. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778

    Google Scholar 

  16. R. Girshick. Fast R-CNN. in IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440–1448

    Google Scholar 

  17. S. Ren, K. He, R. Girshick, et al., Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Machine Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  18. T.Y. Lin, P. Dollar, R. Girshick et al., Feature pyramid networks for object detection. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936–944

    Google Scholar 

  19. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517–6525

    Google Scholar 

  20. W. Liu, D. Anguelov, D. Erhan et al., SSD: single shot multibox detector. in European Conference on Computer Vision (ECCV), 2016, pp. 21–37

    Google Scholar 

  21. K. Shankar, M. Elhoseny, R. Satheesh Kumar, S.K. Lakshmanaprabu, X. Yuan, Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique. J. Ambient. Intell. Humaniz. Comput. (2018). https://doi.org/10.1007/s12652-018-1161-0

  22. K. Shankar, M. Elhoseny, S.K. Lakshmanaprabu, M. Ilayaraja, R.M. Vidhyavathi, M. Alkhambashi, Optimal feature level fusion based ANFIS classifier for brain MRI image classification. Concurrency Comput. Pract. Exp. 2018. https://doi.org/10.1002/cpe.4887

  23. M. Elhoseny, G.-B. Bian, S.K. Lakshmanaprabu, K. Shankar, A.K. Singh, W. Wu, Effective features to classify ovarian cancer data in internet of medical things. Comput. Netw. 159, 147–156 (2019)

    Article  Google Scholar 

  24. N. Krishnaraj, M. Elhoseny, M. Thenmozhi, Mahmoud M. Selim, K. Shankar. Deep learning model for real-time image compression in Internet of Underwater Things (IoUT). J. Real-Time Image Process. 2019. https://doi.org/10.1007/s11554-019-00879-6

  25. Y. Zhu, C. Zhang, D. Zhou, et al., Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214, 758–766 (2016)

    Article  Google Scholar 

  26. J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431–3440

    Google Scholar 

  27. A. Takeki, T.T. Tu, R. Yoshihashi, et al., Combining deep features for object detection at various scales: finding small birds in landscape images. Trans. Comput. Vis. Appl. 8(1), 5 (2016)

    Article  Google Scholar 

  28. J. Wang, X. Wang, K. Zhang, et al., Small UAV target detection model based on deep neural network. J. Northwest. Polytech. Univ. 36(2), 258–263 (2018)

    Article  Google Scholar 

  29. Q. Lu, Y. Liu, J. Huang, X. Yuan, Q. Hu, License plate detection and recognition using hierarchical feature layers from CNN. Multimed. Tools Appl. 78(11), 15665–15680 (2019)

    Article  Google Scholar 

  30. X. Yuan, L. Xie, M. Abouelenien, A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn. 77, 160–172 (2018)

    Article  Google Scholar 

  31. X. Yuan, V. Sarma, Automatic urban water-body detection and segmentation from sparse ALSM data via spatially constrained model-driven clustering. IEEE Geosci. Remote Sens. Lett. 8(1), 73–77 (2010)

    Article  Google Scholar 

  32. G. Cheng, J. Han, P. Zhou, et al., Multi-class geospatial object detection and geographic image classification based on collection of part detectors. J. Photogramm. Remote Sens. 98(1), 119–132 (2014)

    Article  Google Scholar 

  33. H. Sun, X. Sun, H. Wang, et al., Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model. IEEE Geosci. Remote Sens. Lett. 9(1), 109–113 (2011)

    Article  MathSciNet  Google Scholar 

  34. J. Han, P. Zhou, D. Zhang, et al., Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding. J. Photogramm. Remote Sens. 89(1), 37–48 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

The authors were supported in part by the National Natural Science Foundation of China under Grant 61702157, in part by NSF of Hebei Province through the Key Program under Grant F2016202144, in part by NSF of North China Institute of Aerospace Engineering through the Key Program under Grant ZD-2013-05, and in part by Self-financing Program of Langfang under Grant 2018013155.

Author information

Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China

    Yongjun Qi & Junhua Gu

  2. Information Technology Center, North China Institute of Aerospace Engineering, Langfang, China

    Yongjun Qi, Dengchao Feng & Yingru Su

  3. Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China

    Junhua Gu

  4. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

    Zepei Tian

Authors
  1. Yongjun Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junhua Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zepei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dengchao Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingru Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA

    Xiaohui Yuan

  2. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qi, Y., Gu, J., Tian, Z., Feng, D., Su, Y. (2020). Research on Low Altitude Object Detection Based on Deep Convolution Neural Network. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-45099-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45098-4

  • Online ISBN: 978-3-030-45099-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation