  Waterline usually plays as an important visual cue for maritime applications.
However, the visual complexity of inland waterline presents a significant
challenge for the development of highly efficient computer vision algorithms
tailored for waterline detection in a complicated inland water environment.
This paper attempts to find a solution to guarantee the effectiveness of
waterline detection for inland maritime applications with general digital
camera sensor. To this end, a general deep-learning-based paradigm applicable
in variable inland waters, named DeepWL, is proposed, which concerns the
efficiency of waterline detection simultaneously. Specifically, there are two
novel deep network models, named WLdetectNet and WLgenerateNet respectively,
cooperating in the paradigm that afford a continuous waterline image-map
estimation from a single captured video stream. Experimental results
demonstrate the effectiveness and superiority of the proposed approach via
qualitative and quantitative assessment on the concerned performances.
Moreover, due to its own generality, the proposed approach has the potential to
be applied to the waterline detection tasks of other water areas such as
coastal waters.
