# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 5 Discussion  
Visual noises to inland waterlines detection, e.g., linear objects similar to waterline, water ripples, shadow or fog, resulting from the variations of environmental factors usually influence the robustness and stability of the approaches to visually recognize waterlines, thus making it difficult for marine USVs to continually guarantee the effectiveness of vision-based waterline detection approaches in a variable inland water scenario. From the previous experimental results, it can be seen that our deep-learning-based approach achieves much better detection effects compared with traditional approaches. It essentially benefits from an inspiration that deep learning techniques can extract more robust and more discriminative representations (or features) for high-level particular tasks from a large amount of diverse original data by virtue of specific machine learning algorithms, even in the absence of any prior knowledge. Actually, a great deal of current research in computer vision tasks has also confirmed the insight. Motivated with the insight, we proposed a waterline detection approach by devising three specific schemes based on deep learning techniques, i.e., WLpeephole, WLdetectNet and WLgenerateNet, respectively (detailed in Sections 3.1 and 3.2). Among them, WLpeephole is the groundwork of our approach, which accounts for providing WLdetectNet and WLgenerateNet with robust and discriminative waterline features against environmental noises, and further supports their collaboration to effectively accomplish high-level waterline detection tasks. Thus, here, we primarily explain the success of our proposed approach from the perspective of the qualitative visualizations of the deep representations (or features) regarding the WLpeephole-based visually receptive fields under diverse cases. As shown in Figure 9, we visualize the deep representations of a group of waterline positive samples with certain visual complexity, which are extracted by our deep main network WLdetectNet. From the visualization results in the form of heatmaps, we can see that their highlighted areas just correspond to the areas surrounding waterlines in the WLpeephole-based visually receptive fields (i.e., original image patches), which means human-interpretable concepts related to a waterline have emerged as our deep discriminative features that significantly avail high-level accurate detection tasks about the waterline. This observation coincides with our design principle about WLpeephole (de \begin{table}
\begin{tabular}{c c c} \hline _Stability_ **Over** & **Canny Edge Detector** & **Ours** \\ \hline _precision_ & \(-1.767\) & \(-1.153\) \\ _recall_ & \(-3.198\) & \(0.991\) \\ _FP_ & \(1.052\) & \(-1.124\) \\ _irrelevance_ & \(-0.313\) & \(0.196\) \\ \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparisons on stability for edge detection method (with image preprocessing) and ours under varied foggy conditions. tailed in Section 3.1), which also verifies our previous assumption about WLpeephole that necessary contextual information in a receptive field can help extract more discriminative representations about the waterline with the aid of a deep neural network. Furthermore, the success of our proposed approach also benefits to some extent from our insight for the scope of WLpeephole since we constrain the WLpeephole as a local receptive field with a certain visual size which can scan across the whole detected image. The advantages of this design are as follows: first, feeding a WLpeephole-based image patch with smaller size to a deep neural network can reduce its computation costs owing to fewer parameters, thus improving the efficiency of our waterline detection; second, arranging the WLpeephole with an appropriate size can compensate for the imprecision of existing edge (or line) detection approaches owing to relying on specific prior knowledge, thus improving the accuracy of our waterline detection.