# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 4 Experiment Study  
### Experimental Results on MDTU  
The proposed method in this paper is an improvement of the U-Net-based network structure, and the encoder part uses MobileNet for feature extraction, which greatly reduces the number of parameters in the network model. To focus on more semantic information about cracks, we use cross-skip connection and, furthermore, add PA and CA modules in the decoder part to enhance the feature representation of cracks in both spatial and channel dimensions. Finally, the two parts are summed and fused to produce a high segmentation accuracy result. Therefore, U-Net, MobileNetV2-UNet (which also uses MobileNet as the encoder for feature extraction), and DANet (which uses the dual-attention mechanism in parallel), are chosen as experimental comparison models with MA-Xnet to qualitatively verify the performance of the proposed model. The pixel accuracy curves and loss function curves of different methods are shown in Figures 6 and 7. It can be seen in Figure 6 that the growth of each model method stabilizes around the 40 th trip. Compared with U-Net, DANet, and MobileNetV2-UNet, the pixel accuracy curve of the proposed model is the highest, indicating that the performance of the proposed model is effectively improved, compared with that of the baseline network U-Net. Meanwhile, it can be seen in the loss function curves in Figure 7 that the MA-Xnet and the lightweight network MobileNetV2-UNet converge faster, due to the use of the lightweight neural network MobileNet as the feature extractor, compared with the DANet and U-Net with larger network parameters and deeper network layers. Although the convergence speed of MA-Xnet and MobileNetV2-UNet is similar, MA-Xnet makes more effective use of the fracture feature information due to the cross-skip connection and the inclusion of the dual-attention module, which makes it higher than MobileNetV2-UNet in terms of the accuracy curve. On the other hand, Recall, Precision, F1-Score, and mean intersection of union (mIoU) are used to qualitatively analyze the performance of different networks, with the metric comparison shown in Table 2.  
As shown in Table 2, this paper's method outperforms the compared methods in several metrics, especially the key metrics of the F1-Score and the mIoU. Due to the shallow feature information extracted by U-Net, the semantic information features of channels and spatial dimension are not effectively considered, resulting in coarser segmentation results. Compared with U-Net, the proposed model in this paper improves by 11.32% and 12.37% the key indices of the F1-Score and the mIoU, respectively. The number of parameters of the proposed model in this paper is reduced by 82.33% compared with U-Net before the improvement. This is because the use of the bottleneck residual depth-wise separable  
Figure 6: Pixel accuracy curve comparison. Figure 7: Loss function curve comparison. convolution allows the proposed model to effectively avoid the problems of gradient explosion and overfitting due to the deeper layers of the network, thereby improving the operational efficiency of the network. Compared with MobileNetV2-UNet, a lightweight neural network that also applies deep separable convolution, the application of cross-skip connection, and dual-attention mechanism of the proposed model simultaneously increases the weight of useful feature information in the spatial dimensions and channels for cracked images, and thus improves over MobileNetV2-UNet in the F1-Score and the mIoU by 5.31% and 8.06%, respectively. Compared with DANet, which also applies the dual-attention mechanism, the feature extractor and cross-skip connection of the proposed model appear to be more advantageous, with a 4.34% and 6.05% improvement, respectively, in the key indices of the F1-Score and the mIoU over the DANet network.