# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 1 Introduction  
As a part of structures such as bridges, and dams, concrete plays an important role. The stability of concrete determines the safety of people's lives and property. Unfortunately, due to various factors such as climate and load, concrete is corrooded rapidly, resulting in cracks, spalling, and other damages that endanger structures' safety. Therefore, it is necessary to inspect and maintain concrete-poured structures effectively. In the early maintenance strategy, field survey is the main way of inspection, which will consume a lot of manpower and material resources. In addition, the work itself is extremely dangerous, a little carelessness will cause casualties. Automatic concrete damage detection can intelligently report the damage degree of the structures, greatly reduce the expenditures of manpower and material resources, and provide more scientific evidence for the maintenance of the structures. Therefore, the automatic damage detection of concrete has gradually become a research hotspot.  
With the development of computer vision, more and more damage detection algorithms based on computer vision have been developed [1; 2; 3]. Deep convolution neural network (DCNN) is one of the most popular technologies at present. Compared with the traditional detection algorithm, the detection algorithm based on DCNN has more advantages, and more and more researchers are devoted to the research of concrete damage detection algorithm based on DCNN [4; 5; 6]. In the field of concrete crack detection, many researchers classify image patches by convolution neural network (CNN) to obtain the approximate location of cracks. Some researchers exploit CNN model to regress the bounding boxes of cracks. In addition, some researchers regard concrete crack detection as a semantic segmentation task, i.e., classifying pixel points to obtain crack locations.  
Fully Convolutional Networks for semantic segmentation (FCN) [7] has been applied to solve challenges in multidisciplinary fields, such as automatic driving [8] and remote sensing image segmentation [9]. In this paper, a full convolution network based on dilated convolution for concrete crack detection is proposed, which includes an encoding stage and a decoding stage. In the encoding stage, a residual network called Resnet-18 [10] is first utilized to extract the feature map of the input image, and then the dilated convolutions [11] with different dilation rates is exploited to extract the feature maps with different receptive fields. After that, the feature maps are further fused to obtain more semantic information feature maps. In the decoding stage, the fused feature maps from encoder are up-sampled by using the deconvolutions until the size is the same as the input image. After that, the Pixels of up-sampling feature maps are classified by the SoftMax function. To evaluate the performance of the algorithm, we tested it on the public dataset [12]. Unfortunately, the dataset has no semantic information at pixel level, so we selected 600 images from the dataset for manual annotation, and divided the labeled dataset into training set, verification set and test set, with data volumes of 400, 100, and 100 respectively. We first train the proposed model on the training set, and evaluate the performance of the proposed model on the test set after the training. For the sake of evaluating the performance of the algorithm effectively, we introduce the common evaluation indexes for semantic segmentation [13]: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU).