# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 1 Introduction  
Nowadays, as a risk-eliminating and cost-saving tool, unmanned surface vehicles (USVs) [1] play an important role in maritime applications. Meanwhile, in inland marine environment, more and more activities such as hydrologic surveys, harbor surveillance, maritime search and rescue, have witnessed their prevailing success. In general, the USVs are equipped with a variety of sensors (i.e., sensing devices) to capture significant environmental information around them and guide their following correct movements. As an indispensable optical sensor, an ordinary digital camera has been popular for USVs due to its benefits of usage and economy. Among the vision information obtained by this type of sensor, the waterline is one of the most important visual cues for USVs, since it is usually treated as a reference target of sailing to facilitate USVs to carry out numerous critical missions. For example, based on computer vision techniques, the USVs mounted with a camera sensor can accomplish obstacle avoidance and autonomous navigation through recognizing the sailing area from captured optical images. Accordingly, effectively identifying waterlines in images, namely vision-based waterline detection, has been desired to assist USVs to perform anticipated actions including ensuring their own sailing security. However, it is challenging for USVs with camera sensors to achieve satisfactory detection effects within inland water, because of the visual complexity of inland waterlines, such as their own irregularity and versatility, as well as the diversity and dynamics of their surroundings. With the development of computer vision techniques, many waterline detection approaches by virtue of a general digital camera have been proposed. Most of them aim at the detection of coastal waterline in sea areas, e.g., [2; 3; 4; 5], and there are also a few works focusing on inland waterline, e.g., [6; 7; 8]. When applied in inland waters, the detection effects of these approaches tend to be vulnerable to the variations of environmental factors (e.g., weather conditions such as fog, snow or rain, illumination conditions such as shadow, reflection or water glint, the shapes of waterlines, as well as the viewpoints of cameras). The reason is that the erratic environmental factors usually engender more visual complexity on the inland waterline. For example, the visual information of the background surrounding an inland waterline might become more confusing due to the change of illumination. Correspondingly, the stability of existing approaches is prone to be disturbed in such changeable and complicated inland water scenarios. Specifically, existing vision-based waterline detection approaches generally share a pipeline that consists of two relevant processes, i.e., waterline-relevant feature representation and discriminative strategy (or algorithm) for final identification, respectively. For instance, some approaches [4; 9; 10] considered waterline detection as an issue on edge detection, wherein they firstly extracted the features pertinent to edge (or line) according to specific prior knowledge or statistical assumptions, and then identified the edge (or line) capable of approximately fitting the waterline within an image via a discriminative strategy determined in advance or algorithm associated with the previously represented features. Moreover, there are a number of other vision-based approaches that treat waterline detection as an image segmentation task [11; 12]. They similarly followed the pipeline comprising feature representation and discriminative strategy. Nevertheless, current proposals for the two relevant processes in these vision-based approaches tend to overwhelm the stability of the approaches themselves, due to their deficiencies in the robustness against variable inland water environments. The deficiencies of the proposals are summarized as follows:  
1.