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SDA-Net: a detector for small, densely distributed, and arbitrary-directional ships in remote sensing images

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Abstract

Ships in remote sensing images are usually arranged in arbitrary direction, with small objects and dense distribution, and are easily interfered by background noise, which makes the existing object detection methods have a certain missed detection rate and false detection rate in this kind of complex scene. In order to solve the above problems, a neural network for ship detection is proposed. The network is composed of symmetrical deformation feature pyramid network, multi-scale attention network, multi-scale local context network and rotation branch. Firstly, in order to fully extract the features of ships with arbitrary directions and small objects in remote sensing images, an adaptive deformable convolution unit is designed and a symmetrical deformation feature pyramid network is constructed with it as core. Secondly, a multi-scale attention module is designed to guide the network to focus on ship areas of different scales and suppress background noise. Then, a multi-scale local context network is designed to capture the correlation between ships and local nearby objects, and to learn multi-scale co-occurrence features. Finally, a rotation bounding box is generated by the rotation branch, which is used to mark ships in arbitrary direction. The experimental results on two remote sensing public datasets DOTA, HRSC2016 show that the proposed method achieves state-of-the-art accuracy.

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References

  1. Lu HL, Li YN, Li H (2019) Ship detection by an airborne Passive Interferometric Microwave Sensor (PIMS). IEEE Trans Geosci Remote Sens 58(4):2682–2694

    Article  MathSciNet  Google Scholar 

  2. Zhang T, Yang Z, Gan HP (2020) PolSAR ship detection using the joint polarimetric information. IEEE Trans Geosci Remote Sens 58(11):8225–8241

    Article  Google Scholar 

  3. Wang CL, Bi FK, Zhang WP (2017) An intensity-space domain CFAR method for ship detection in HR SAR images. IEEE Geosci Remote Sens Lett 14(4):529–533

    Article  Google Scholar 

  4. Leng X, Ji K, Zhou S (2016) An adaptive ship detection scheme for spaceborne SAR imagery. Sensors 16(9):1345

    Article  Google Scholar 

  5. Li QP, Mou LC, Liu QJ (2018) HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery. IEEE Trans Geosci Remote Sens 56(12):7147–7161

    Article  Google Scholar 

  6. Zhang ZH, Guo WW, Zhu SN (2018) Toward arbitrary-oriented ship detection with rotated region proposal and discrimination networks. IEEE Geoscience Remote Sensing Letters 15(11):1745–1749

    Article  Google Scholar 

  7. Cui ZY, Li Q, Cao ZJ (2019) Dense attention pyramid networks for multi-scale ship detection in SAR images. IEEE Trans Geosci Remote Sens 57(11):8983–8997

    Article  Google Scholar 

  8. Guo HY, Yang X, Wang NN (2020) A Rotational Libra R-CNN Method for Ship Detection. IEEE Trans Geosci Remote Sens 58(8):5772–5781

    Article  Google Scholar 

  9. Wang JW, Yang WH, Li HC, Zhang HJ, Xia GS (2021) Learning center probability map for detecting objects in aerial images. IEEE Trans Geosci Remote Sens 59(5):4307–4323

    Article  Google Scholar 

  10. Girshick R, Donahue J, Darrell T, Malik J, Vision, Recognition P (2014) (CVPR), Columbus, OH, 580-587. https://doi.org/10.1109/CVPR.2014.81

  11. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  12. Girshick R (2015) Fast R-CNN. IEEE International Conference on Computer Vision (ICCV), Santiago, 1440-1448. https://doi.org/10.1109/ICCV.2015.169

  13. He K, Gkioxari G, Dollár P (2017) R. Girshick, Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), Venice, 2980-2988. https://doi.org/10.1109/ICCV.2017.322

  14. Redmon J, Farhadi A (2017) YOLO9000: Better, Faster, Stronger. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 6517-6525. https://doi.org/10.1109/CVPR.2017.690

  15. Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:1804.02767. Available: https://www.arxiv.org/abs/1804.02767

  16. Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: Optimal speed and accuracy of object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv preprint arXiv:2004.10934

  17. Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. European conference on computer vision (ECCV), 9905, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2

  18. Jiang Y, Zhu X, Wang X (2017) R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  19. Ma JQ (2020) RRPN++: Guidance towards more accurate scene text detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  20. Li C, Cong RM, Guo CL (2020) A parallel down-up fusion network for salient object detection in optical remote sensing images. Neurocomputing 415(20):411–420

    Article  Google Scholar 

  21. Ding J, Xue N, Long Y, Xia G, Lu Q (2019) Learning RoI transformer for oriented object detection in aerial images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2844-2853. https://doi.org/10.1109/CVPR.2019.00296

  22. Yang X (2019) SCRDet: Towards more robust detection for small, cluttered and rotated objects. IEEE International Conference on Computer Vision (ICCV), 8231-8240. https://doi.org/10.1109/ICCV.2019.00832

  23. Ming Q, Miao L, Zhou Z (2021) Cfc-net: A critical feature capturing network for arbitrary-oriented object detection in remote sensing images. arXiv preprint arXiv:2101.06849

  24. Han J, Ding J, Xue N (2021) Redet: A rotation-equivariant detector for aerial object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2786-2795

  25. Yang X, Sun H, Fu K, Yang J, Sun X, Yan M, Guo Z (2018) Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens 10(1):132

    Article  Google Scholar 

  26. Han X, Dai Q (2018) Batch-normalized Mlpconv-wise supervised pre-training network in network. Appl Intell 48:142–155. https://doi.org/10.1007/s10489-017-0968-2

    Article  Google Scholar 

  27. Cui Z, Sun HM, Yu JT (2021) Fast detection method of green peach for application of picking robot. Appl Intell. https://doi.org/10.1007/s10489-021-02456-6

    Article  Google Scholar 

  28. Ma N, Zhang X, Zheng HT, Sun J (2018) ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. European conference on computer vision (ECCV), 11218, 116-131. https://doi.org/10.1007/978-3-030-01264-9_8

  29. Ma J, Shao W, Ye H (2018) Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans Multimed 20(11):3111–3122

    Article  Google Scholar 

  30. Xia GS (2017) DOTA: A large-scale dataset for object detection in aerial images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3974-3983. https://doi.org/10.1109/CVPR.2018.00418

  31. Liu ZK, Yuan L, Weng LB, Yang YP (2017) A high resolution optical satellite image dataset for ship recognition and some new baselines. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 324–331

  32. Yedida R, Saha S, Prashanth T (2021) LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence. Appl Intell 51:1460–1478. https://doi.org/10.1007/s10489-020-01892-0

    Article  Google Scholar 

  33. Lin T, Goyal P, Girshick R, He K (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318–327. https://doi.org/10.1109/TPAMI.2018.2858826

    Article  Google Scholar 

  34. Tian Z, Shen C, Chen H, He T (2019) FCOS: Fully convolutional one-stage object detection. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 9626-9635. https://doi.org/10.1109/ICCV.2019.00972

  35. Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  36. Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. Conference and Workshop on Neural Information Processing Systems (NIPS), 379-387

  37. Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra R-CNN: Towards balanced learning for object detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 821-830. https://doi.org/10.1109/CVPR.2019.00091

  38. Zhang H, Chang H, Ma B (2020) Dynamic R-CNN: Towards high quality object detection via dynamic training. Proceedings of the European conference on computer vision (ECCV), 2. arXiv preprint arXiv:2004.06002

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

    Article  Google Scholar 

  40. Yu Y, Yang X, Li J, Gao X (2020) A cascade rotated anchor-aided detector for ship detection in remote sensing images. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2020.3040273

    Article  Google Scholar 

  41. Cui ZY, Leng JX, Liu Y (2021) SKNet: detecting rotated ships as keypoints in optical remote sensing images. IEEE Trans Geosci Remote Sens 1–15. https://doi.org/10.1109/TGRS.2021.3053311

  42. Yang X, Yan JC, Feng ZM, Recognition P (2019) (CVPR). arXiv preprint arXiv:1908.05612

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Acknowledgements

The authors are grateful for collaborative funding support from the Humanity and Social Science Foundation of the Ministry of Education, China (21YJAZH077).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhe Cui, Hong-Mei Sun, Ruo-Nan Yin & Rui-Sheng Jia

  2. Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, 266590, Qingdao, China

    Hong-Mei Sun & Rui-Sheng Jia

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  1. Zhe Cui
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  2. Hong-Mei Sun
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  4. Rui-Sheng Jia
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Correspondence to Hong-Mei Sun.

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Cui, Z., Sun, HM., Yin, RN. et al. SDA-Net: a detector for small, densely distributed, and arbitrary-directional ships in remote sensing images. Appl Intell 52, 12516–12532 (2022). https://doi.org/10.1007/s10489-021-03148-x

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