# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 1 Introduction
To improve the accuracy of waterline detection, we innovatively devise two significant schemes (presented in Section 3.1) for the materialization of WLdetectNet by following the same architectural principles with many modern deep CNNs (e.g., MobileNet [20] and ResNet [21]), which are actually dedicated to the improvement of the representational capability of this deep learning model for varied waterlines. At the same time, owing to its architectural characteristics benefiting from the two specialized schemes, WLdetectNet lays stress on the efficiency of waterline detection as well. What is more, to improve the robustness of WLdetectNet for variable inland water environments, another deep neural network, i.e., WLgenerateNet, is built to assist the improvement of the generalization ability of WLdetectNet in diverse cases by exploiting the classical generative adversarial networks, such as DCGAN [22]. In brief, the overall design for the proposed paradigm is inspired by the current great successes of deep learning techniques in computer vision applications, especially the modern CNNs for object detection [23] and the classical GANs for image generation [24]. To summarize, the main contributions of this paper are as follows. First, we model the challenging issue on the visual detection of a waterline as an end-to-end machine learning task based on deep neural networks by proposing an alternative waterline detection paradigm (DeepWL) for inland marine USVs, which makes the waterline-related feature  
Figure 1: The diagram of our proposed DeepWL for waterline detection. representation and its subsequent waterline discrimination automatically work together in one unified deep network model (WLdetectNet) without any priors or assumptions, and simultaneously achieves their robustness and stability for diverse cases in complicated inland waters via a scalable and generalizable training set constructed by another deep network model (WLgenerateNet). Second, based on the proposed paradigm, we present an algorithm to conduct waterline image-map estimation from video frames captured on board, which formulates the learning task on vision-based waterline detection as a sequence of repetitive subtasks on distinguishing the segments relevant to a waterline (i.e., waterline segments). Importantly, to ensure the effectiveness and efficiency of our waterline detection algorithm, we propose two significant schemes to implement the mainstay of this paradigm (i.e., WLdetectNet): one of them concentrates on the visual perception ability of the deep network, and the other scheme aims to bolster the representational capability of the deep network with little extra computational cost. Finally, we define relevant metrics specialized for the quantitative evaluation of the visual waterline detection approach on related performances, and then conduct empirical investigations on the effectiveness and superiority of the proposed approach via qualitative and quantitative assessment. Compared with other alternative approaches, the proposed approach achieves better robustness and stability in the presence of environmental noises in variable inland water. Moreover, we explain the success of the proposed approach for the vision-based waterline detection task in such scenarios from the perspective of visualization, and discuss its case study as well. The remainder of this paper is organized as follows. Section 2 introduces the related work on vision-based waterline detection and architecture of deep neural networks. The details of the proposed approach are presented in Section 3. Experimental results and evaluation are illustrated in Section 4, followed by the discussion in Section 5. Section 6 concludes the paper.