# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 2 Proposed Method  
### Deep Learning Model  
#### 2.1.1 Network Component and Design  
Our network SHSnet is a type of encoder-decoder network, and its overall architecture is shown in Figure 1. It consists of 4 units: (a) encoding units (ENCs), (b) decoding units (DECs), (c) large receptive fields (RFs) + boundary refinement units (BNs), and (d) weighed multiscale 'attention' (WMA). The design philosophy and working mechanisms of each of these units are discussed as follows. ENCs: It should be noted that the richness (amount) of contextual information is controlled by the depth and scale of the encoder requiring large computational resources. Our goal is to develop a network design that not only could deal with complex cracks but also could not be computationally expensive. Encoding blocks of SHSnet are inverted residual blocks; we first widen them with a \(1\times 1\) convolution, then use a \(3\times 3\) depth-wise convolution to greatly reduce the number of parameters, and then again use another \(1\times 1\) convolution to reduce the number of channels so that input and output can be added, leading to a residual structure similar to EfficientNet [46]. These blocks are divided into 5 groups (ENC 1-5) that encode the image at 5 different scales, as shown in Figure 1.  
DECs: The DECs' role is thus to up-sample the low-resolution segmentation map to a resolution that is the same as that of the input resolution. This will ensure that each pixel in the input layer will have a corresponding pixel in the output layer. The decoding unit consists of a transposed convolution layer, as shown in Figure 1.  
Figure 1: Architecture of the proposed SHSnet for segmentation of thin, tortuous, and dense cracks. Here, w, h, n, and c are width, height, no. of channels, and no. of classes, respectively. Large RFs: Conventional deep learning models have small RFs, even when they are built deeper [47] (Figure 2A). A conventional solution to circumvent this issue is to use a large input image at the cost of high computational power, which would require expensive hardware. However, when the input image size is lower, this affects the segmentation accuracy (e.g., in Table 2), especially for complex cracks. Our method solves these problems with a large kernel that will increase the effective receptive field (RF) (Figure 2B), thus facilitating segmentation [47]. To do so, a global convolution network (GCN) module is employed. In the GCN, CNNs are stacked in configurations 1 \(\times\) K + K \(\times\) 1 and K \(\times\) 1 + 1 \(\times\) K, and finally, the output of these parallel units is added, as shown in Figure 1. This enables dense connections within a large K \(\times\) K region in the input map, i.e., 'large kernels'. Compared to the conventional 'k \(\times\) k' convolution, the GCN module requires significantly fewer parameters and thus is more practical in our case. BNs: The BN module is a residual structure. More specifically, the output refined the scoring map S_out as S_out = S_in + R(S_in), where S_in is the coarse input score map and R(\(\cdot\)) is the residual branch. It helps with improving the delineation of cracks from the background. WMA: Residual-like connections for 'attention' were used as shown in Figure 1 to capture long-range features across multiple scales and boost feature representation capabilities;