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  • A Method for Object Segmentation in Low-Resolution Images Based on Mask R-CNN

    Paper number

    IAC-19,B1,IP,17,x50937

    Author

    Ms. Ting Da, China, Xi'an Microelectronics Technology Institute, China Aerospace Science and Technology Corporation (CASC)

    Coauthor

    Mr. Yang Liang, China

    Coauthor

    Dr. Liang Yang, China, Xi'an Microelectronics Technology Institute, China Aerospace Science and Technology Corporation (CASC)

    Year

    2019

    Abstract
    A novel method for object segmentation in low-resolution images based on Mask R-CNN is proposed. Although the existing methods have made remarkable progress in high-resolution images and large-sized objects, the performance on low-resolution images or small objects is far from satisfactory. Since it is difficult to obtain the high-resolution images in many cases, we aim to pick objects in low-resolution images with higher accuracy using deep learning. A new model, which is designed to combine the bicubic interpolation and Mask R-CNN, is developed to improve the picking performance in cityscape images. The experimental results show that the proposed method can get a better performance compared to existing algorithms.
    Abstract document

    IAC-19,B1,IP,17,x50937.brief.pdf

    Manuscript document

    (absent)