# SCCDNet: A Pixel-Level Crack Segmentation Network
###### Abstract  
Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the feature map shaving the same resolution in the Encoder and Decoder. We used depthwise separable convolution to improve the accuracy of crack segmentation while reducing the complexity of the model. In this paper, a dataset containing cracks collected in different scenes was established, and SCCDNet was trained and tested on this dataset. Compared with the advanced models, SCCDNet obtained the best crack segmentation performance, while F-score reached 0.7763.  
crack detection; semantic segmentation; SCCDNet; SSE module; DenseNet +
Footnote †: journal: Medicalalways decrease. For instance, the FoSA algorithm and the CrackTree algorithm have good detection results for fine cracks, but the detection accuracy for thick cracks will decrease. To overcome the limitations of traditional computer vision algorithms, the Convolutional Neural Networks (CNNs) are applied to vision tasks. CNNs were first proposed by [PERSON] et al. [10], which can yield abundant hierarchical features in the image, and then can better understand the objects in the image [11]. CNNs are widely used in many spheres such as image classification [12; 13], object detection [14], semantic segmentation [15; 16; 17], and are gradually being applied to crack detection tasks. In order to detect cracks timely, researchers design classifiers to determine whether image units belong to cracks. The network designed by [PERSON] et al. [18] based on CNNs combined with sliding window technology can detect the crack images whose resolutions are greater than 256 \(\times\) 256; the classifier designed by [PERSON] et al. [19] can detect crack images with a resolution of 3264 \(\times\) 2448; [PERSON] et al. [20] used supervised learning to train CNNs classifiers and discussed the impact of regularization methods such as unsupervised hierarchical pre-training and training set expansion on the results. However, these image-level crack detection algorithms can only judge whether there are cracks in small image units and cannot accurately segment the cracks in pixel-level, so it is impossible for them to analyze the characteristics of cracks such as length and width. In addition to algorithms based on CNNs, Artificial Neural Networks (ANNs) [21; 22; 23; 24] and Support Vector Machines (SVMs) [25; 26] have been verified to be able to classify crack images, which have the same problems. In some cases, we need to calculate the cracks' length and width so that we could evaluate the safety of the targets. Therefore, we have to do pixel-level crack detection instead of path-level crack detection to achieve this target. This kind of pixel-level crack detection can be equivalent to semantic segmentation task. U-Net [27] is a common semantic segmentation network. It uses an Encoder-Decoder structure and the skip-connection strategy to concatenate the shallow feature maps with the deeper feature maps, which greatly improves the network performance, but at the same time, it will increase model parameters; SegNet [28] also uses the skip-connection strategy to introduce the maximum pooling parameter in the Encoder module into the Decoder module, greatly improving the computational efficiency of the model, but at the expense of detection accuracy. [PERSON] et al. [29] proposed a crack detection method based on the model of semantic segmentation, which could alleviate the influence of the trace of tie-rod holes and formworks on the detecting accuracy.