## 5 Conclusions  
Pavement cracks are one of the early defects of roads that affect service life and driving safety. For practical engineering applications, pavement management departments need to check a large number of road miles. Huge model parameters and computational overhead are unaffordable or unnecessary. Considering that the DCNN models usually adopt a single-feature modeling method, a lightweight real-time segmentation network for pavement cracks is designed that adopts three methods of asymmetric convolution, convolution, and involution in different depths of the neural network. The core is to extract the low-level features of cracks in the shallow layers, and strengthen the expression of high-level features in the deep layers. The main work and conclusions of this study are as follows:  
* To better reflect the real situation of road inspection, in Section 3.1, we construct the pavement defect dataset through field research and photography. Pavement materials include asphalt mixtures and cement concrete, and environmental disturbances were composed of shadows, water stains, and tire marks. This is beneficial to reflect the generalization performance and general applicability of the model. * Convolution and involution have different extraction characteristics for crack features. In the shallow network, convolution is better for the detail extraction of cracks, focusing on channel-dimension modeling, while involution has a strengthening effect on the backbone information of cracks, focusing on spatial-dimension modeling. * In Section 4.1, the experiment shows that the use of the involution under mini-batch training caused the validation set to not converge, and the output segmented binarized image had dead pixels. The built-in batch normalization of involution to group normalization, named involution-G, is changed to avoid the impact caused by differences in data distribution. * To obtain the optimal position of the designed REI module, the results of the comparative experiments in Section 3.5 show that the effect is better when the REI module occupies 1/3 of the baseline model. Compared with the model using only convolution, MIOU, MPA, recall, and F1 score are improved by 0.0242, 0.92%, 0.0263, and 0.013, respectively. In Section 4.2, we analyzed the difference between involution-G and convolution from the perspectives of inference speed and feature construction, and explain why the REI module is effective in deep networks. * The field of view of asymmetric convolution is more sensitive to cracks. Through the designed ACE module, the extraction of crack features in shallow networks is enhanced. In Section 3.6, the results show that by adding the ACE module, the MIOU for the model was improved by 0.0281, the MPA by 0.18%, the recall by 0.0443, and the F1 score by 0.0209. * The proposed RIIAnet model is compared with semantic segmentation networks of different structures. The evaluation indicators and visualization results of Section 3.7 show that the MIOU, MPA, recall, and F1 score of RIIAnet reach 0.7705, 0.9868, 0.8047, and 0.8485, respectively. Its parameter amount is 0.04 times that of U-Net, and it can run in real time under high-resolution images. In neural networks, the essence of network design is the mechanism used to allocate computing resources. For example, the attention mechanism in deep learning aims to emulate human vision [55], and the network performance can be improved by giving more attention to the target region at the particle level. Our proposed model improves the network performance by giving different attention patterns to different levels of the network at the macro level. The model proposed in this paper can reallocate limited computing resources in the spatial and channel feature modeling methods to achieve the effect of improving model performance with fewer parameters.