# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 1 Introduction  
Over the years, many technologies have been developed to characterize damages in concrete infrastructures. These can be classified based on the sensing principle utilized: stress waves [1, 2], electrical properties [3, 4], optical fibers [5, 6], or digital image correlation (DIC) [7]. Among these techniques, the vision-based technique is considered apt because of its simple implementation as well as direct and easy result interpretation. The literature on vision-based systems of crack characterization is vast and ever-growing. A more detailed review can be found in reference [8]. Generally speaking, studies such as [9, 10, 11] have reinforced that deep learning models based on convolutional neural networks (CNNs) outperform machine learning models, feed-forward neural networks (FNNs), and conventional image processing techniques. Recently, many techniques based on deep learning methodologies have been proposed [12]. They have shown stellar performance in many different scenarios on normal concrete cracks. These models can be classified based on architecture: (A) mask R-CNNs [13], which require pixel-level labels and bounding boxes as ground truth, leading to incompatibility with this dataset and others; (B) convolutional autoencoders (such as [14, 15]), which need to be trained on a large sample size of non-textured backgrounds and compute pixel-wise defects as the difference between the reconstructed image and the original image, working well with large defects on non-textured backgrounds [15] but not suitable for this work; and (C) encoder-decoder architectures, the most common for developing segmentation models for normal cracks [12], with different variations as discussed below. Researchers working on normal concrete cracks collected from various parts of the world (for example, [PERSON] et al. [16] in Denmark, [PERSON] [17] in the USA, [PERSON] et al. [18] in Turkey, [PERSON] et al. [19] in China, and [PERSON] [20] in Singapore) found that VGG16-based models work well for the identification and segmentation of RC cracks. The FPHBN [21] model is a variation of VGG16-based models, where features at different scales are pooled to create a segmentation map. Other notable models include DeepCrack [22], CrackForest [23], and CrackTree [24]. SDDnet [25], loosely based on the DeepLabV3+ [26] architecture, is superior to these models and has a lower computational cost. Other computationally efficient crack segmentation models based on DeepLabV3+ include [PERSON]'s Model [27], which uses ResNet18 for the segmentation of pavement cracks in the Philippines, and a similar model by [PERSON] et al. [28] for segmenting cracks in concrete dams. [PERSON] et al. [29] created an encoder using depth-wise convolutions adapted from MobileNet [30], while [PERSON] et al. [31] used MobileNet as an encoder to improve parameter efficiency in the segmentation of normal cracks. UNet is another popular model for semantic segmentation, specifically targeting medical images [32]. More recently, the use of attention for superior medical image segmentation has been demonstrated through Attention-UNet [33], which adds an attention module to UNet, and UNet++, which uses a nested design and further improves computational efficiency through pruning. The importance of attention to improving crack segmentation was also validated with CrackSegnet [34] and CrackNex [35]. Cracks significantly differ in shape; thus, [PERSON] et al. [36] proposed CrackUNet by tailoring UNet for crack segmentation. This model is superior in segmenting cracks in reinforced concrete structures compared to many other deep learning networks. Although there have been recent attempts to reduce the parameter space of deep learning models, such as UNet++ [37], IDSnet [38], and MixCrackNet [39], it remains unknown whether they can deal with complex cracks rather than complex backgrounds, which is significantly different, as will be discussed later.