Deep Visual Waterline Detection within Inland Marine Environment
This addresses the challenge of visual complexity in inland waterline detection for maritime applications, though it appears incremental as it builds on existing deep learning methods.
The paper tackled waterline detection in complex inland marine environments by proposing DeepWL, a deep-learning paradigm with two novel network models, WLdetectNet and WLgenerateNet, which achieved effective and superior performance in continuous waterline estimation from video streams.
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.