Skip to main content
SpringerLink
Log in

MS-SSD: multi-scale single shot detector for ship detection in remote sensing images

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Object detection is a fundamental problem in computer vision. Although impressive results have been achieved on large/medium-sized objects, the detection performance of small objects remains a challenging task. Automatic ship detection on remote sensing images is an important module in maritime surveillance system, and it is challenging due to the high variance in appearance and scale. In this work, we thoroughly discuss the issues of SSD on multi-scale objects and propose a multi-scale single-shot detector (MS-SSD) to improve the detection effect of small ship targets and enhance the model’s robustness to scale variance. It enjoys two benefits by introducing (1) more high-level context and (2) more appropriate supervision. Extensive experiments on the Airbus Ship Detection Challenge dataset demonstrate the effectiveness of the proposed method in ship detection from complex backgrounds in remote sensing images. We also achieve better detection performance on the COCO dataset, outperforming state-of-the-art approaches, especially for small targets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Wang X, Kong T, Shen C, Jiang Y, Li L (2020) Solo: Segmenting objects by locations. In: European conference on computer vision. Springer, pp 649–665

  2. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg A C (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

  3. Zhu Y, Zhao C, Wang J, Zhao X, Wu Y, Lu H (2017) Couplenet: Coupling global structure with local parts for object detection. In: Proceedings of the IEEE international conference on computer vision, pp 4126–4134

  4. Zhang S, Wen L, Bian X, Lei Z, Li S Z (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4203–4212

  5. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

  6. Han J, Liang K, Zhou B, Zhu X, Zhao J, Zhao L (2018) Infrared small target detection utilizing the multiscale relative local contrast measure. IEEE Geosci Remote Sens Lett 15(4):612–616

    Article  Google Scholar 

  7. Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho K (2019) Augmentation for small object detection. CoRR, arXiv:1902.07296

  8. Chen C, Liu M-Y, Tuzel O, Xiao J (2016) R-cnn for small object detection. In: Asian conference on computer vision. Springer, pp 214–230

  9. Hu G X, Yang Z, Hu L, Huang L, Han J M (2018) Small object detection with multiscale features. International Journal of Digital Multimedia Broadcasting

  10. Bai Y, Zhang Y, Ding M, Ghanem B (2018) Sod-mtgan: Small object detection via multi-task generative adversarial network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 206–221

  11. Pal S K, Pramanik A, Maiti J, Mitra P (2021) Deep learning in multi-object detection and tracking: state of the art. Appl Intell:1–30

  12. Tian G, Liu J, Zhao H, Yang W (2021) Small object detection via dual inspection mechanism for uav visual images. Appl Intell:1–14

  13. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  14. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99

    Google Scholar 

  15. Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R (2019) A survey of deep learning-based object detection. IEEE access 7:128837–128868

    Article  Google Scholar 

  16. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  17. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  18. Fu C-Y, Liu W, Ranga A, Tyagi A, Berg A C (2017) Dssd: Deconvolutional single shot detector. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–11

  19. Yuxi Li J L, Lin W (2018) Tiny-DSOD: Lightweight object detection for resource-restricted usage. In: BMVC

  20. Erhan D, Szegedy C, Toshev A, Anguelov D (2014) Scalable object detection using deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2147–2154

  21. Bell S, Zitnick C L, Bala K, Girshick R (2016) Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2874–2883

  22. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CoRR, arXiv:1409.1556

  23. Hosang J, Benenson R, Schiele B (2017) Learning non-maximum suppression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4507–4515

  24. Adelson E H, Anderson C H, Bergen J R, Burt P J, Ogden J M (1984) Pyramid methods in image processing. RCA Eng 29(6): 33–41

    Google Scholar 

  25. Singh B, Davis L S (2018) An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3578–3587

  26. Yang Y, Ramanan D (2012) Articulated human detection with flexible mixtures of parts. IEEE Trans Pattern Anal Mach Intell 35(12):2878–2890

    Article  Google Scholar 

  27. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 886–893

  28. Ding Y, Xiao J (2012) Contextual boost for pedestrian detection. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE, pp 2895–2902

  29. Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545

    Article  Google Scholar 

  30. Felzenszwalb P F, Girshick R B, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  31. Yang J, Wu B, Li L, Cao P, Zaiane O (2021) Msds-unet: A multi-scale deeply supervised 3d u-net for automatic segmentation of lung tumor in ct. Comput Med Imaging Graph:101957

  32. Li X, Zhao L, Wei L, Yang M-H, Wu F, Zhuang Y, Ling H, Wang J (2016) Deepsaliency: Multi-task deep neural network model for salient object detection. IEEE Trans Image Process 25 (8):3919–3930

    Article  MathSciNet  MATH  Google Scholar 

  33. Sun C, Ai Y, Wang S, Zhang W (2021) Mask-guided ssd for small-object detection. Appl Intell 51(6):3311–3322

    Article  Google Scholar 

  34. Wang G, Xiong Z, Liu D, Luo C (2018) Cascade mask generation framework for fast small object detection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1–6

  35. Dong J, Chen Q, Yan S, Yuille A (2014) Towards unified object detection and semantic segmentation. In: European conference on computer vision. Springer, pp 299–314

  36. Sistu G, Leang I, Yogamani S (2018) Real-time joint object detection and semantic segmentation network for automated driving. Adv Neural Inf Process Syst:1–5

  37. Uijlings JRR, Van De Sande KEA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

  38. Li Z, Zhou F (2017) Fssd: feature fusion single shot multibox detector. CoRR, arxIv:1712.00960

  39. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

  40. Zhang Z, Qiao S, Xie C, Shen W, Wang B, Yuille A L (2018) Single-shot object detection with enriched semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5813–5821

  41. Wang H, Wang Q, Gao M, Li P, Zuo W (2018) Multi-scale location-aware kernel representation for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1248–1257

  42. Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PHS (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2805–2813

  43. Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387

  44. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  45. Xie E, Sun P, Song X, Wang W, Liu X, Liang D, Shen C, Luo P (2020) Polarmask: Single shot instance segmentation with polar representation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12193–12202

  46. Chen X, Girshick R, He K, Dollár P (2019) Tensormask: A foundation for dense object segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 2061–2069

  47. Wang S, Gong Y, Xing J, Huang L, Huang C, Hu W (2020) Rdsnet: A new deep architecture forreciprocal object detection and instance segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 12208–12215

  48. Chen K, Lin W, Li J, See J, Wang J, Zou J (2020) Ap-loss for accurate one-stage object detection. IEEE Trans Pattern Anal Mach Intell 43(11):3782–3798

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (No.62076059) and the Science Project of Liaoning Province (2021-MS-105).

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, China

    Guangqi Wen, Peng Cao, Haonan Wang & Hanlin Chen

  2. Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China

    Guangqi Wen & Peng Cao

  3. Alibaba A.I. Labs, Hangzhou, China

    Xiaoli Liu

  4. Communication and Connected Enterprises Division Research Institute, Neusoft, Foshan, China

    Jinghui Xu

  5. Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada

    Osmar Zaiane

Authors
  1. Guangqi Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haonan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hanlin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoli Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinghui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Osmar Zaiane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Cao.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wen, G., Cao, P., Wang, H. et al. MS-SSD: multi-scale single shot detector for ship detection in remote sensing images. Appl Intell 53, 1586–1604 (2023). https://doi.org/10.1007/s10489-022-03549-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03549-6

Keywords

Search

Navigation