# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 1 Introduction
The current proposals for representing waterline-relevant features are hand-crafted that largely depend on specific prior knowledge or statistical assumptions, whereas the applied prior knowledge or assumptions cannot hold in all cases. For example, the waterline was viewed as a horizon line in [4; 13]. Despite the prior knowledge facilitates representing the features relating to the water-sky-line, it is not always correct in other waterline detection tasks, e.g., water-land-line detection in inland waters. As another example, some studies [14; 15] resorted to classical edge detection algorithms for waterline detection. Similar to conventional feature representation for edges, their means of representing waterline-relevant features usually built on statistical assumptions related to image gradient information, such as classic Canny operators [16]. Although they rendered satisfactory results in real-world tasks, the waterline-relevant features represented in this way are sensitive to noise brought about by the variations of environmental factors. Thus, the discriminative ability of the waterline-relevant features is limited to their applicable scenarios. In addition, before applying the proposals to extract waterline-relevant features, the original images obtained by the USVs generally need to be preprocessed, e.g., image enhancement or denoising, which might impact the efficiency of waterline detection in USVs more or less. 2. The currently used discriminative strategies (or algorithms) to finalize waterline identification mostly work in particular water conditions, which have little consideration for coping with the visual versatility of waterline caused by the variations of environmental factors. For example, in [17], albeit the researchers devised a sophisticated approach to represent waterline-relevant features via classical image structure and texture analysis after image preprocessing, their proposed discriminative strategy is relatively simple and rigid. Specifically, they utilized an empirical value as the threshold of image segmentation to finally discriminate the waterline. Similarly, ref. [6] used an improved maximum or minimum gradient value sample method as the tenacious selecting rule of waterline candidate pixels. Regarding the discriminative strategies(or algorithms) built on the previously extracted waterline-relevant features, as water environments are varying, their ability to identify a waterline might be degraded or even fail. Hence, the discriminative strategies (or algorithms) are inflexible, which cannot be broadly applicable to varied inland water environments simultaneously. With the motivation of tackling the above deficiencies, this paper aims to guarantee the effectiveness of waterline detection for the USVs with an ordinary digital camera in variable inland water environments via machine learning techniques. To achieve this, we make an attempt to improve the robustness and stability of waterline detection for diverse cases by proposing a general deep-learning-based paradigm for inland marine USVs, named DeepWL, which concerns the efficiency of waterline detection simultaneously. As illustrated in Figure 1, the proposed paradigm consists of two cooperative deep neural network models. One, termed WLdetectNet, is customized as the primary network (i.e., a learning model) for our deep visual waterline detection by exploiting convolutional neural networks (CNNs) [18]. The other one, named WLgenerateNet, is built upon generative adversarial networks (GANs) [19] that serves as the auxiliary network (i.e., another learning model) of WLdetectNet. As the mainstay of this paradigm, the WLdetectNet is modeled as an end-to-end deep convolutional neural network to directly achieve the identification of candidate waterlines in each image captured by USVs, rather than separating waterline-relevant feature representation from its related discriminative strategy (or algorithm), and without resorting to image preprocessing.