## 4 Discussion
### Analysis of the Efficiency of the REI Module  
From the experimental results of Section 3.5, when building lightweight real-time segmentation networks, we believe the REI module does not play an obvious role in the shallow feature extraction module for the following reasons:  
In terms of inference speed, we input the same data to the convolution and involution-G operators, respectively, to test their inference time. The experimental conditions include inputting the same data calculation amount, inputting the same size feature maps with a different number of channels, and inputting different size feature maps with the same number of channels. The results are shown in Figures 16-18, respectively:  
Figure 16: Inference speed of convolution and involution-G operators under the same amount of data calculation.  
We can find that the number of channels has a large impact on the inference speed of convolution, while the involution-G operator is more sensitive to the feature map size. The convolution weights are determined by fitting the data in the training phase, but the parameters in involution-G are dynamically calculated from the input data, which affects the inference speed of the involution-G operator when computing high-resolution feature maps. In addition, that involution-G requires more data reading-and-writing operations when performing inference compared to convolution. What is more, when involution-G is used for computation in shallow layers, the computational cost increases abruptly when large-resolution feature maps are unfolded, resulting in an obvious influence on the inference speed of the involution-G operator.  
In terms of feature expression, as shown in Section 3.3, since involution focuses on modeling the spatial dimension and weakens the modeling relationship in the channel  
Figure 17: Inference speed of convolution and involution-G operators under the same size feature maps with a different number of channels.  
Figure 18: Inference speed of convolution and involution-G operators under different size feature maps with the same number of channels.  
dimension, this affects the ability of involution to interact with information in the low channel dimension and has some influence on expressing the feature diversity in the shallow space.  
The idea underlying convolution is to form global information from the combination of features in the local sensory field to make target judgments, and the computing power is allocated more to the channel dimension [54]. However, it was found in [39] that as the depth of the network increases, the relevance of the convolutional kernels becomes stronger, the diversification decreases, and the redundant information between channels becomes prominent. This means that the computing power is somewhat wasted in the deep network constructed by convolution; in contrast, involution can use larger convolutional kernels to increase the perceptual field without increasing the number of parameters, and use the sharing of weight parameters between channels to encode different semantic information to enhance spatial modeling and allocate more computing power to the spatial dimensions.  
We combine the different characteristics of these two operators by employing convolution in the shallow network to integrate channel information and enrich the details of the crack feature by using the advantages of local pattern modeling in the high-resolution feature map; in the deep levels, we adopt the involution operator to take advantage of the large convolution kernel in the low-resolution feature map for long-range dynamic interaction modeling and exploit its unique spatial specificity to enhance the representation of the semantic information. This structure not only makes full use of the computing resources in the channel and spatial dimensions but also avoids the shortcomings of conventional convolution and involution in deep neural networks by amplifying each of them.