Attention Mask R-CNN for ship detection and segmentation from remote sensing images

Xuan Nie, Mengyang Duan, Haoxuan Ding, Bingliang Hu, Edward K. Wong

    Research output: Contribution to journalArticlepeer-review


    In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target's features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively.

    Original languageEnglish (US)
    Article number8951182
    Pages (from-to)9325-9334
    Number of pages10
    JournalIEEE Access
    StatePublished - 2020


    • Computer vision
    • object detection
    • object segmentation
    • remote sensing

    ASJC Scopus subject areas

    • General Computer Science
    • General Materials Science
    • General Engineering


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