2019/11/24 by Jing Huang, Huang, Jing, Hengfeng Miao +7
Computer Science · Engineering · #Advanced Neural Network Applications #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Environmental science #FOS: Computer and information sciences #Geography #Geology #Image Enhancement Techniques #Oceanography #Remote sensing #Underwater Vehicles and Communication Systems #Waterline #cs.CV
paper · pdf · doi:10.48550/arxiv.1911.10498
9 pages, 3 figures, journal
arxiv created 2019/11/24 · openalex publication_date 2019/11/24 · arxiv updated 2019/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.