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This paper proposes a hierarchical reasoning method for shoreline extraction that relies solely on a camera and visual algorithms. First, the DeepLabV3+ algorithm is used to extract water bodies from images, and the Canny operator is applied to outline these water bodies. Subsequently, the YOLOv8-seg algorithm is used to extract contours of objects encountered in the image, helping to eliminate occlusions caused by these objects. Finally, the KeyPoint-RCNN algorithm is employed to distinguish between the left and right shorelines, achieving the extraction of the shoreline. For key point detection, a creation method of datasets is proposed that improves the target and key point detection capabilities of the KeyPoint-RCNN algorithm by 27.1% and 2.2%, respectively.

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Study on Shoreline Extraction in Inland Waterway Navigation Scenarios

  • 会议论文

  • 发表日期：

    2025年

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  • Guang Wei,
  • Feng Ma,
  • Hua Guo

摘要

This paper proposes a hierarchical reasoning method for shoreline extraction that relies solely on a camera and visual algorithms. First, the DeepLabV3+ algorithm is used to extract water bodies from images, and the Canny operator is applied to outline these water bodies. Subsequently, the YOLOv8-seg algorithm is used to extract contours of objects encountered in the image, helping to eliminate occlusions caused by these objects. Finally, the KeyPoint-RCNN algorithm is employed to distinguish between the left and right shorelines, achieving the extraction of the shoreline. For key point detection, a creation method of datasets is proposed that improves the target and key point detection capabilities of the KeyPoint-RCNN algorithm by 27.1% and 2.2%, respectively.