To address the false alarm and missed detection problems caused by large differences in ship types, ships side by side and complex backgrounds in optical remote sensing images, a High-Efficiency Channel Attention Network (HECAN) model is proposed in this paper. We enhance the detection capability of the model for small-scale ships based on the EfficientDet model by using an efficient channel attention optimized feature extraction network to solve the channel information loss problem. Meanwhile, the Soft-NMS is used to improve the non-maximum suppression algorithm of the model to improve the recall rate of side-by-side ships. The results are compared with other object detection models and show that the HECAN model detects better on small-scale and side-by-side ships with an average precision of 92.46% and and a recall rate of 4.75% higher than the original EfficientDet.


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    Title :

    Optical Remote Sensing Image Ship Detection Method for Improving EfficientDet


    Contributors:
    Xie, Guobo (author) / Li, Jiaying (author) / Xiao, Feng (author) / Li, Tianyu (author)


    Publication date :

    2021-11-12


    Size :

    571133 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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