# MA-Xnet: Mobile-Attention X-Network for Crack Detection
###### Abstract  
Modern crack detection algorithms based on deep learning have unsolved issues, such as an abundance of parameters in the resulting models and lack of context information. Such issues may lower the efficiency of feature extraction and lead to unexpected task performance. Based on two semantic segmentation models, U-Net and the dual-attention network (DANet), an efficient mobile-attention X-network (MA-Xnet) is proposed for crack detection. For performance evaluation, segmentation experiments were performed on concrete crack images from an internationally recognized dataset, which were collected from various campus buildings of Middle East Technical University. The experimental results demonstrated that, compared with U-Net, the proposed method parameters were reduced by 82.33%, and improved by 11.32% and 12.37% in the key indices of the Fl-Score and the mean intersection of union (mIoU), respectively, providing a reference for subsequent related lightweight crack-segmentation research. Keywords:concrete cracks; semantic segmentation; dual attention; deep learning +
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improve the efficiency of crack detection by eliminating the need for manual measurement of features in crack images. U-Net [14] started as a depth-developed FCN in the field of medical image segmentation. U-Net has become a benchmark for image segmentation in different fields. In particular on the application of the U-Net model for pavement crack detection has received wide attention due to a series of advantages, such as simplicity, efficiency, and ease of construction [15; 16; 17]. In concrete crack-detection experiments, [PERSON] et al. [18] demonstrated that U-Net spends less training time and performs better than other FCN methods [12; 13]. The U-Net model uses VGG-Net as the feature extraction network. Although it can extract effective feature information, due to its too-abundant weight parameters, the model operation is large, causing the network's training to take a long time. In addition, the convolution method in U-Net has limited detail extraction for features, and it is susceptible to the loss of some fine features when the features are recovered. To be able to integrate more semantic information without increasing the number of parameters, [PERSON] et al. [19; 20; 21] proposed a series of deep CNN (DCNN) models called DeepLab. The core of these models is to use atrous convolution; i.e., expanding the receptive field of the convolution kernel by inserting holes in the convolution. However, the disadvantage of DeepLab is that it tends to cause grid effects. [PERSON] et al. [22] improved DCNN and applied it to the semantic segmentation of cracks without any pre-processing cracks. As the utilized models become increasingly deeper, the complexity also increases. To improve the performance and reduce the number of parameters in these models, Google has proposed the lightweight CNN MobileNets series [23; 24; 25], focusing on the mobile use and deployment of CNNs. In 2020, [PERSON] et al. [26] proposed an extremely efficient CNN, Mobile-U-Net, to achieve the segmentation of textile defects. Mobile-U-Net is based on the U-Net architecture, and the encoder is replaced by MobileNetV2. This method greatly improves the segmentation speed of the model and achieves high performance. To further improve segmentation accuracy and effectively utilize contextual semantic information, [PERSON] et al. [27] proposed a dual-attention network (DANet), which added two types of attention modules to the traditional expanded FCN to adaptively integrate local features and global dependencies. Most of the deep-learning-based crack segmentation methods [28; 29; 30; 31; 32] are improvements on the encoder-decoder-based U-Net. However, the deeper layers of the network itself and the huge parameters of these methods make them very limited when applied to crack detection, and they are prone to missed detections or false detections, due to the lack of full utilization of feature information during feature recovery.