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Object Detection in Maritime Scenarios Based on Swin-Transformer

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6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021) (CCIE 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 920))

Abstract

Object detection is a favourite research technology in the field of maritime computer vision and current research mostly adopts region models based on convolutional neural networks (CNNs). Swin-transformer brings a new and effective approach, with its hierarchical structure and attention mechanism-based block, well compensating for the weakness of CNNs that cannot take into account global features. In this paper, we propose a method for applying the Swin-Transformer to maritime object detection, obtaining significantly improved results on the well-known Seaships dataset. Experiments demonstrate that Swin-Transformer, after pre-trained and fine-tuning, has a detection mean average precision of 96.61%, outperforming other CNNs. Furthermore, Swin-Transformer has demonstrated its capability to detect features of the irregular ship shapes in complex backgrounds, and the detection of general cargo ships and passenger ships is excellent.

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References

  1. Qiao, D., Liu, G., Lv, T., Li, W., Zhang, J.: Marine vision-based situational awareness using discriminative deep learning: a survey. J. Mar. Sci. Eng. 9(4), 397 (2021)

    Article  Google Scholar 

  2. Shao, Z.F., Wang, L.G., Wang, Z.Y., Du, W., Wu, W.J.: Saliency-aware convolution neural network for ship detection in surveillance video. IEEE Trans. Circuits Syst. Video Technol. 30(3), 781–794 (2020)

    Article  Google Scholar 

  3. Thompson, D.J.: Maritime object detection, tracking, and classification using lidar and vision-based sensor fusion. PhD dissertations and master’s theses, p. 377 (2017)

    Google Scholar 

  4. Prasad, D.K., Prasath, C.K., Rajan, D., et al.: Object detection in a maritime environment: performance evaluation of background subtraction methods. IEEE Trans. Intell. Transp. Syst. 20(5), 1787–1802 (2019)

    Article  Google Scholar 

  5. Cane, T., Ferryman, J.: Evaluating deep semantic segmentation networks for object detection in maritime surveillance. In: Proceeding of 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 1–6, CentAUR (2019)

    Google Scholar 

  6. Zhang, W., He, X., Li, W., Zhang, Z., Wang, P.: An integrated ship segmentation method based on discriminator and extractor. Image Vis. Comput. 93, 103824 (2020)

    Article  Google Scholar 

  7. Hu, H.M., Guo, Q., Zheng, J., Wang, H.Z., Li, B.: Single image defogging based on illumination decomposition for visual maritime surveillance. IEEE Trans. Image Process. 8(6), 2882–2897 (2019)

    Article  MathSciNet  Google Scholar 

  8. Zhang, Y., Li, Q.Z., Zang, F.N.: Ship detection for visual maritime surveillance from non-stationary platforms. Ocean Eng. 141(1), 53–63 (2017)

    Article  Google Scholar 

  9. Liu, Z., Waqas, M., Yang, J., Rashid, A., Han, Z.: A multi-task CNN for maritime target detection. IEEE Signal Process. Lett. 28, 434–438 (2021)

    Article  Google Scholar 

  10. Li, S.L., Guo, Y.P., Xu, Y., Li, Z.L.: Real-time geometry identification of moving ships by computer vision techniques in bridge area. Smart Struct. Syst. 23(4), 359–371 (2019)

    Google Scholar 

  11. Li, Q.Z., Xu, X.Y.: Fast detection of surface ship targets based on improved YOLOV3-Tiny. Comput. Eng. 47(10), 283–289 (2020)

    Google Scholar 

  12. Xu, Z., Zhang, W., Zhang, T., Yang, Z., Li, J.: Efficient transformer for remote sensing image segmentation. Remote Sens. 13, 3585 (2021)

    Article  Google Scholar 

  13. Shao, Z.F., Wu, W.J., Wang, Z.Y., Du, W., Li, C.Y.: SeaShips: a large-scale precisely-annotated dataset for ship detection. IEEE Trans. Multimedia 20(10), 1 (2018)

    Article  Google Scholar 

  14. Dilip, K., Prasad, D.K., Rachmawati, L., Rajabally, E., Quek, C.: Video processing from electro-optical sensors for object detection and tracking in a maritime environment: a survey. IEEE Trans. Intell. Transp. Syst. 18(8), 1993–2016 (2017)

    Article  Google Scholar 

  15. Zhao, H.W., Zhang, W.S., Sun, H.Y., Xue, B.: Embedded deep learning for ship detection and recognition. Future Internet 11(2), 53 (2019)

    Article  Google Scholar 

  16. Wang, N., Wang, Y., Er, M.J.: Review on deep learning techniques for marine object recognition: architectures and algorithms. Control Eng. Pract. 118, 104458 (2020)

    Article  Google Scholar 

  17. Soloviev, V., Farahnakian, F., Zelioli, L., Iancu, B., Lilius, J., Heikkonen, J.: Comparing CNN-based object detectors on two novel maritime datasets. In: Proceeding of 2020 IEEE International Conference on Multimedia & Expo Workshops, ICMEW, pp. 1–6 (2020)

    Google Scholar 

  18. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv:2103.14030 (2021)

  19. Zhou, H., Lu, C., Yang, S., Yu, Y.: ConvNets vs. transformers: whose visual representations are more transferable? arXiv:2108.05305 (2021)

  20. Xie, J., Wu, Z., Zhu, R., Zhu, H.: Melanoma detection based on Swin Transformer and SimAM. In: Proceeding of 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC, pp. 1517–1521 (2021)

    Google Scholar 

  21. Wang, P., Ji, L., Ji, Z., Gao, Y., Liu, X.: 1st place solutions for UG2+ challenge 2021–(semi-)supervised face detection in the low light condition. arXiv:2107.00818 (2021)

  22. Koay, H.V., Huang, C.J., Chow, C.O.: Shifted-window hierarchical vision transformer for distracted driver detection. In: Proceeding of 2021 IEEE Region 10 Symposium, TENSYMP, pp. 1–7 (2021)

    Google Scholar 

  23. Liu, S., Zhou, H., Li, C., Wang, S.: Analysis of anchor-based and anchor-free object detection methods based on deep learning. In: Proceeding of IEEE International Conference on Mechatronics and Automation, ICMA, pp. 1058–1065 (2020)

    Google Scholar 

  24. Moosbauer, S., Knig, D., Jaekel, J., Teutsch, M.: A benchmark for deep learning based object detection in maritime environments. In: Proceeding of 15th IEEE Workshop Perception Beyond the Visible Spectrum, pp. 916–925 (2019)

    Google Scholar 

  25. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards realtime object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  26. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceeding of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)

    Google Scholar 

  27. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceeding of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9756–9765 (2020)

    Google Scholar 

  28. He, K., Zhang, X., Ren, S., Jian, S.: Deep residual learning for image recognition. In: Proceeding of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, IEEE Press, Seattle (2016)

    Google Scholar 

  29. Liang, T.T., et al.: CBNetV2: a composite backbone network architecture for object detection. arXiv:2107.00420 (2021)

  30. Song, G.L., Liu, Y., Wang, X.G.: Revisiting the sibling head in object detector. In: Proceeding of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11560–11569 (2020)

    Google Scholar 

  31. Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. arXiv:2101.01169 (2021)

  32. Vaswani, A., et al.: Attention is all you need. In: Proceeding of 31st International Conference on Neural Information Processing Systems, pp. 6000–6010, ACM, Beijing (2017)

    Google Scholar 

  33. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2021)

  34. Lin, T., Wang, Y., Liu, X., Qiu, X.P.: A survey of transformers. arXiv:2106.04554 (2021)

  35. Tan, F., et al.: SDNet: mutil-branch for single image deraining using swin. arXiv:2105.15077 (2021)

  36. Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., Timofte, R.: SwinIR: image restoration using swin transformer. arXiv:2108.10257 (2021)

  37. Wu, M., Qian, Y., Liao, X., Wang, Q., Heng, P.: Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention. arXiv:2111.03368 (2021)

  38. Xu, M.D., et al.: End-to-end semi-supervised object detection with soft teacher. arXiv:2106.09018 (2021)

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Authors and Affiliations

  1. Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Wenli Sun

  2. National Engineering Research Center of Maritime Navigation System, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Xu Gao

Authors
  1. Wenli Sun
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  2. Xu Gao
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Correspondence to Wenli Sun .

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Editors and Affiliations

  1. Universidad de Guanajuato, Guanajuato, Mexico

    Yuriy S. Shmaliy

  2. Ain Shams University, Cairo, Egypt

    Abdelhalim Abdelnaby Zekry

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Sun, W., Gao, X. (2022). Object Detection in Maritime Scenarios Based on Swin-Transformer. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_77

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  • DOI: https://doi.org/10.1007/978-981-19-3927-3_77

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3926-6

  • Online ISBN: 978-981-19-3927-3

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