• Electronics Optics & Control
  • Vol. 27, Issue 5, 102 (2020)
GONG Ming1, LIU Yanyan1, and LI Guoning2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3969/j.issn.1671-637x.2020.05.020 Cite this Article
    GONG Ming, LIU Yanyan, LI Guoning. A Ship Detection Method for Remote-Sensing Images Based on Improved YOLO-v3[J]. Electronics Optics & Control, 2020, 27(5): 102 Copy Citation Text show less

    Abstract

    Focusing on the ship target detection of remote-sensing images, a real-time remote sensing detection method based on YOLO-v3 was proposed.By introducing the spatial pyramid pooling structure, and by using the dense connection and Inception structure to achieve dimension reduction transition module, the extraction of network feature information was enhanced.By re-replacing the connection structure of the backbone network and optimizing the multi-scale feature fusion detection, a new network structure was designed, which reduced the parameter quantity, enhanced the feature transmission, and finally achieved an effect better than the original method.The remote sensing image dataset provided by Airbus in the Kaggle competition was used for the comparison test.The result showed that, the average detection accuracy of the algorithm is 84%, the accuracy is improved by about 4% compared with the original algorithm, and the speed of detection reaches 23 frames per second.
    GONG Ming, LIU Yanyan, LI Guoning. A Ship Detection Method for Remote-Sensing Images Based on Improved YOLO-v3[J]. Electronics Optics & Control, 2020, 27(5): 102
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