# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 1 Introduction
A detailed survey and the training requirements of these models are reported in Table 1. The following conclusions were drawn:  
1. Most crack segmentation models are developed to detect wide cracks; there are rarely any models demonstrated for complex cracks. 2. Even though there is a large volume of models published, they are not optimized for complex crack segmentation. 3. Most of the models require modern hardware for training, which is not always available in various laboratories. 4. Training resolutions are large enough to identify context and have become essential for crack segmentation. It is known that cracks that are tortuous, have high density, are thin (micro), and have high contrast between these features (HCRF) occurring within the same crack segment are harder to characterize. A crack with one or more of these features is hereby referred to as a complex (micro)crack. This is confirmed by our previous work [9] as well as by other researchers [40; 41] who found that due to additional complexity, networks developed for normal concrete cracks do not work or perform suboptimally for the characterization of complex cracks. Complex cracks occur in many infrastructures, including buildings, pavement, dams, bridges, and tunnels [42; 43; 44]. Unfortunately, due to additional complexity, the methods discussed above do not perform optimally in these situations. The development of techniques capable of the automatic characterization of thin, high-density, tortuous, HRCF microcracks (especially under loads) thus remains an open problem in the literature. Conventional wisdom suggests that deeper networks solve more complex problems. A huge deep learning model would certainly be able to solve the segmentation of complex cracks but would need large computing resources, not to mention large-scale labeled datasets that need years of development. This makes it challenging to incorporate these models in practical conditions such as in industrial/construction applications and makes it inaccessible to not well-equipped domain-specific research laboratories, i.e., impractical. In this work, we present an alternative method that is not limited by the restrictions of conventional methodologies. More specifically, a new pipeline to characterize complex (micro)cracks is developed. Our contributions are as follows:  
1. A novel computational model is presented that has been specifically tailored for the high-efficiency pixel-wise segmentation of complex cracks and the characterization of these cracks in incomplete/inaccurate segmentation maps in highly imbalanced datasets. 2. SHSnet is developed to achieve the segmentation of complex cracks. It consists of (A) a large receptive field that allows better learning of contextual features, (B) boundary refinement units to achieve better boundaries, (C) residual multiscale connections for 'attention' for long-range features and boost feature representation capability, and (D) a novel loss function (LF) to train the network efficiently on a highly imbalanced dataset with HCRF cracks. Our network design is novel and different from SOTA models (as discussed in the previous section) for crack segmentation. 3. A novel post-processing unit (PPU) is developed for crack morphological parameter calculations. The PPU is based on fracture mechanics, and geometric properties are