# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology
### The Main Network WLdetectNet
#### 3.1.2 Deepening WLdetectNet to Improve Its Own Representational Capability  
It is generally believed that improving their own representational capability of learning models is a predominant means to bolster the specific performance of tasks based on machine learning, such as accuracy on prediction. As a prosperous deep learning model, CNNs have been extensively applied in a variety of computer vision tasks including object detection [31; 32]. In addition, numerous current research works on CNNs [33; 34; 35] have demonstrated that extending the depth of this deep learning model, namely increasing the number of network layers, is the most straightforward way to improve its representational power. The reason is that the deeper neural networks usually can fit more complicated nonlinear mapping from inputs to outputs that indicates the more robust representational power corresponding to the deep networks. Accordingly, to bolster some particular performances of learning tasks, many prominent variants of the basic deep neural networks have been proposed in this way, such as ResNet [21] for accurate image classification, and DeeptransMap [36] for robust single image dehazing.  
Inspired by the great success of extending the depth of CNNs, in this paper, we innovatively constitute a rather deep architecture for WLdetectNet to guarantee its robust representational capability, thus improving the accuracy of the main network for waterline detection. A complete description of its architectural specification is presented in Table 1. It is worth mentioning that, similar to many modern variants of CNNs, some critical architectural principles are adopted in the construction of the deep network. For example, to extend the depth of WLdetectNet, we repeatedly exploit several structural modules in the residual branch of WLdetectNet such as ResNet [21]. Then, to facilitate training such a deep network, we similarly take advantage of the shortcut path to back-propagate gradients. Meanwhile, in order to alleviate the information loss in such a deep network as much as possible to ensure the effectiveness and efficiency of this deep network in diverse cases, within each of the repeated structural modules, we attempt to successively make use of pointwise group convolution (i.e., PGconv [28]), channel shuffle operation (i.e., Shuffle [28]), depthwise convolution (i.e., Dwconv [20]), point convolution (i.e., Pconv [20]), global average pooling (i.e., GAP), fully connected operations (i.e., FC, with a Relu and Sigmoid activation, respectively) and channel-wise scaling operation (i.e., Scale [35]) to enrich and equalize the information flow in the main network of our proposed paradigm. Especially, the reasonable utilization of PGconv [28] and Dwconv [20] in our architectural design of WLdetectNet benefits reducing the number of network parameters and computational complexity, which are crucial for guaranteeing the efficiency of the deep network. Actually, despite being deepened, WLdetectNet has little extra computational cost, about 3.12 MFLOPs (i.e., the number of floating-point multiplication-adds) which is very suitable for the inland USVs with computationally limited application. In addition, the channel operation Shuffle [28] adopted in our architectural design also contributes to ensure the robust representational capability of WLdetectNet by equalizing the information flow in such a deep network.  
As shown in Table 1, WLdetectNet uses an individual image region perceived in accordance with WLpeephole as its input (i.e., the first layer of the deep network), and at its last layer outputs a scalar value indicating the category of the corresponding region, namely waterline or non-waterline. Moreover, apart from its input and output, the overall architecture of WLdetectNet is a linear stack of five repeatable structural modules, which totally consists of 72 convolutional layers. Because of following the common architectural principles of modern deep CNNs such as ResNet [21], WLdetectNet is easy to be constructed and trained.