Fast Ship Detection in Optical Remote Sensing Images Based on Sparse MobileNetV2 Network

  • Jinxiang Yu
  • Tong Yin
  • Shaoli Li
  • Shuo Hong
  • Yu PengEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)


Ship detection in optical remote sensing images (ORSIs) has drawn lots of attention because of its extensive potential in maritime applications. Although many methods have been proposed in recent years, there are still great challenges for improving the detection accuracy and detection speed. In this paper, a fast ship detection method in optical remote sensing images based on sparse MobileNetV2 network is proposed, which has high accuracy and fast detection speed. Ship detection problem is turned into a sub-image classification one, which successfully avoids the massive computation caused by the region proposal stage in previous methods. The sparse MobileNetV2 network has high detection accuracy and less computation benefited from the convolutional neural networks and the depth separable convolution. Furthermore, the pruning method is used to compress the network to decrease model complexity and prevent overfitting. Several experiments are conducted based on some optical remote sensing images from Google Earth. The results demonstrate that the proposed method achieves over 5x speed enhancement compared with several mainstream ship detection methods, while the accuracy is competitive.


Optical remote sensing image Ship detection Sparse MobileNetV2 network Model compression 


  1. 1.
    Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS J. Photogram Remote Sens. 117, 11–28 (2016)CrossRefGoogle Scholar
  2. 2.
    Demirel, H., Anbarjafari, G.: Satellite image resolution enhancement using complex wavelet transform. IEEE Geosci. Remote Sens. Lett. 7(1), 123–126 (2010)CrossRefGoogle Scholar
  3. 3.
    Ji-yang, Y., Dan, H., Lu-yuan, W., et al.: A real-time on-board ship targets detection method for optical remote sensing satellite. In: 2016 IEEE 13th International Conference on Signal Processing (ICSP), pp. 204–208. IEEE (2016)Google Scholar
  4. 4.
    Leninisha, S., Hinz, S., Stilla, U.: Vehicle detection in very high resolution satellite images of city areas. IEEE Trans. Geosci. Remote Sens. 48, 2795–2806 (2010)CrossRefGoogle Scholar
  5. 5.
    Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD netowrks. IEEE Trans. Geosci. Remote Sens. 54(10), 5832–5845 (2016)CrossRefGoogle Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRefGoogle Scholar
  7. 7.
    Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  8. 8.
    Liu, W., Anguelov, D., Erhan, D., et al.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37 (2016)Google Scholar
  9. 9.
    Biswas, D., Su, H., Wang, C., et al.: An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD. Phys. Chem. Earth 110, 176–184 (2018)CrossRefGoogle Scholar
  10. 10.
    Sandler, M., Howard, A., Zhu, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jinxiang Yu
    • 1
  • Tong Yin
    • 1
  • Shaoli Li
    • 1
  • Shuo Hong
    • 1
  • Yu Peng
    • 1
    Email author
  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina

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