AROA: Attention Refinement One-Stage Anchor-Free Detector for Objects in Remote Sensing Imagery | SpringerLink
Skip to main content

AROA: Attention Refinement One-Stage Anchor-Free Detector for Objects in Remote Sensing Imagery

  • Conference paper
  • First Online:
Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

Included in the following conference series:

Abstract

Object detection in remote sensing images is a typical computer vision application, which has broad requirements in practice. Recently, attention mechanisms have been widely utilized in a diverse range of visual tasks such as object detection and semantic segmentation. Aimed at the characteristics of remote sensing objects such as rotation variations and inter-class similarity, a question we ask is, what kind of attention mechanism do we really need? In this article, we propose a novel attention refinement one-stage anchor-free object detector (AROA) that leverages attention mechanisms to refine the performance of remote sensing object detection in a one-stage anchor-free network framework. Specifically, we first design an asymmetric spatial self-attention (A\(\text {S}^2\)A) mechanism to capture rich long-range spatial contexts and eliminate the rotate distortion. Then, to solve the issue of inter-class similarity and boost the multiclass identification capability, we propose a channel attention mechanism, named chain-connected channel attention (\(\text {C}^3\)A), which connects the adjacent attention blocks like a chain and dramatically mines the channel relationships. In addition, we also introduce an IoU-wise module (IM) to strengthen the correlation between localization and classification branches and filter out the detected boxes with low positioning quality. Extensive experimental results on the DOTA and NWPU VHR-10 datasets demonstrate the effectiveness of the proposed AROA.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 14871
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 18589
Price includes VAT (Japan)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 91–99 (2015)

    Google Scholar 

  2. Yang, X., et al.: SCRDet: towards more robust detection for small, cluttered and rotated objects. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8232–8241 (2019)

    Google Scholar 

  3. Liu, W., et al.: SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  4. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  5. Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision, pp. 734–750 (2018)

    Google Scholar 

  6. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

    Google Scholar 

  7. Wei, H., Zhang, Y., Wang, B., Yang, Y., Li, H., Wang, H.: X-LineNet: detecting aircraft in remote sensing images by a pair of intersecting line segments. IEEE Trans. Geosci. Remote Sens. 9(2), 1645–1659 (2020)

    Article  Google Scholar 

  8. Chen, J., Xie, F., Lu, Y., Jiang, Z.: Finding arbitrary-oriented ships from remote sensing images using corner detection. IEEE Geosci. Remote Sens. Lett. 17(10), 1712–1716 (2019)

    Article  Google Scholar 

  9. Shi, F., Zhang, T., Zhang, T.: Orientation-aware vehicle detection in aerial images via an anchor-free object detection approach. IEEE Trans. Geosci. Remote Sens. 59(6), 5221–5233 (2020)

    Article  Google Scholar 

  10. Li, Y., Huang, Q., Pei, X., Chen, Y., Jiao, L., Shang, R.: Cross-layer attention network for small object detection in remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14, 2148–2161 (2020)

    Google Scholar 

  11. Wang, J., Wang, Y., Wu, Y., Zhang, K., Wang, Q.: FRPNet: a feature-reflowing pyramid network for object detection of remote sensing images. IEEE Geosci. Remote Sens. Lett. (2020)

    Google Scholar 

  12. Wu, Y., Zhang, K., Wang, J., Wang, Y., Wang, Q., Li, Q.: CDD-Net: a context-driven detection network for multiclass object detection. IEEE Geosci. and Remote Sens. Lett. (2020)

    Google Scholar 

  13. Zhang, X., Wang, G., Zhu, P., Zhang, T., Li, C., Jiao, L.: GRS-Det: an anchor-free rotation ship detector based on Gaussian-mask in remote sensing images. IEEE Trans. Geosci. Remote Sens. 59(4), 3518–3531 (2020)

    Article  Google Scholar 

  14. Zhou, L., Wei, H., Li, H., Zhao, W., Zhang, Y., Zhang, Y.: Arbitrary-oriented object detection in remote sensing images based on polar coordinates. IEEE Access 8, 223373–223384 (2020)

    Article  Google Scholar 

  15. Zhang, G., Lu, S., Zhang, W.: CAD-Net: a context-aware detection network for objects in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 57(12), 10015–10024 (2019)

    Article  Google Scholar 

  16. Lin, Y., Feng, P., Guan, J.: IENet: interacting embranchment one stage anchor free detector for orientation aerial object detection (2019). arXiv:1912.00969

  17. Wei, H., Zhang, Y., Chang, Z., Li, H., Wang, H., Sun, X.: Oriented objects as pairs of middle lines. ISPRS-J. Photogramm. Remote Sens. 169, 268–279 (2020)

    Article  Google Scholar 

  18. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points (2019). arXiv:1904.07850

  19. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  20. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  21. Xia, G., et al.: DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983 (2018)

    Google Scholar 

  22. Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS-J. Photogramm. Remote Sens. 117, 11–28 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61701524, 62006245 and in part by the China Postdoctoral Science Foundation under Grant 2019M653742. Thanks to the anonymous reviewers for their valuable suggestions.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, X., Ma, S., He, L., Zhang, F., Liu, X., Ru, L. (2021). AROA: Attention Refinement One-Stage Anchor-Free Detector for Objects in Remote Sensing Imagery. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87355-4_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87354-7

  • Online ISBN: 978-3-030-87355-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics