# Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces  
[PERSON]  
The Department of Electrical Engineering, National United University, Miaoli 360301, Taiwan; [EMAIL_ADDRESS]
segmentation and instance segmentation. For crack detection, semantic segmentation, which performs pixel-wise identification for all target objects of the crack/non-crack class, is typically preferred. Fully convolutional networks (FCNs) trained via end-to-end learning first demonstrated state-of-the-art performances in 2015 [16]. Subsequently, they were investigated extensively to solve challenging problems in semantic segmentation, such as crack detection at the pixel level. FCNs are an extension of the original CNN and achieve pixel-level classification on the basis of convolution layers [1]. [PERSON] and [PERSON] [17] proposed an FCN with an encoder, i.e., the VGG16 [18], for concrete crack detection and density evaluation. Furthermore, the crack path was accurately monitored using an FCN-based method. In [19], a deep CNN comprising an FCN with an encoder-decoder architecture was proposed for pixel-wise classification to detect cracks. Similarly, the VGG-Net was adopted in this FCN model. [PERSON] et al. [20] introduced an FCN based on a dilated convolution operation. They used a residual network [21] to obtain the feature maps of an input image and performed the dilated convolution operations with different dilation rates to extract feature maps under different receptive fields. CrackSegNet is an end-to-end deep network that combines multiple techniques, including feature extraction by convolution, receptive field expansion by dilated convolution, multi-scale max-pooling, and skipped connections of feature map fusion [1]. Compared with the existing FCN-based models, it offers the advantages of low generalization error and less data requirement; however, it requires more time for the inference process. On the basis of the well-known segmentation model SegNet [22], [PERSON] et al. [23] developed DeepCrack, which is a deep CNN, by learning high-level features for crack representation. To obtain both sparse and continuous features in each scale, DeepCrack added skip layers to connect the encoder to the decoder. The effectiveness of this skipping mechanism in distinguishing cracks from the background within an image was validated. To accommodate binary semantic segmentation, the U-Net was first proposed in [24] to achieve better segmentation results on biomedical images. The U-Net requires relatively few annotated images since it utilizes the elastic deformations of the training samples. The success of the U-Net for biomedical images motivated researchers to evaluate the performance of the U-Net in different applications, such as crack detection. [PERSON] et al. [25] were the first to apply the U-Net to detect concrete cracks. Furthermore, they verified that the U-Net outperformed other existing deep CNNs in terms of robustness, effectiveness, and detection accuracy. Hitherto, both FCNs and U-Nets have been investigated extensively, e.g., the automatic pixel-level crack detection network [26] and the convolutional encoder-decoder network [27]. Although the methods used yielded good performance in concrete crack detection, they require a significant amount of development to be usable in practical applications. For instance, considerable effort is required in acquiring training data, particularly for annotating images in semantic segmentation. Each pixel of interest is labeled with the class of its enclosing region using annotation tools. Hence, another critical issue in crack detection segmentation is data labeling for the training set. [PERSON] et al. [28] presented a pseudo-labeling technique to generate structured pseudo-labels with unlabeled or weakly labeled data. In [29], a self-supervised structure learning network that can be trained without using a GT was introduced. This is achieved by training a reverse network to return the output to the input.