# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 6 Conclusions  
The characterization of cracks becomes convoluted due to inherent complexities such as (interactions from) high-density cracking, thin crack widths, and the tortuosity of cracks. In SHCCs, these complexities are inherent and occur together.  
In this work, a novel deep learning model for the end-to-end segmentation of thin, tortuous, and dense microcracks in diverse conditions is developed. SHSnet is a weighted, 'attention'-based, highly efficient, large receptive field, boundary-refining deep learning model that is strategically designed for the end-to-end segmentation of complex cracks. SHSnet achieves accuracy, IOU, and BF scores of 0.88, 0.81, and 0.84, respectively.  
A novel region-based loss function is adapted from the focal Tversky function, which gives equal class weightage and unequal weightage to easy and complex regions embedded in the network. This ensures that the network consistently learns the crack features from a highly unbalanced dataset and that the performance is not adversely affected by a high range of crack feature (HRCF) complexities.  
A PPU was developed for crack morphological parameter calculation from incomplete/inaccurate segmentation maps using the physics of concrete fracture and crack geometric properties and the scanning lines derived from it. The relative error observed for crack number, length, and width is 0.4%, 1.2%, and 5.6%, respectively.  
A comparative analysis shows that, in general, using attention and proper design improves crack segmentation as compared to the vanilla model. SHSnet, which adds an LF and a large receptive field apart from attention, performs superior to models in the literature while requiring significantly lower parameters with similar capability.  
By applying the SHSnet on samples with different textures, color (dark to light) crack density (up to \(\sim\)300/m) widths (30-300 \(\upmu\)m), and image sources, it was possible to show that there is no bias in the proposed network for certain types of systems. In all cases, segmented cracks match well with the original picture. Beyond the result, a novel way to test generality by using laboratory samples in accelerated testing was explored. This could be an alternative method for data generation, but it needs to be explored further.  
The proposed methodology can also be applied to test the evolution of crack characteristics relevant to durability, as it matches well with those computed manually during the inspection of roads and buildings.  
In the near future, this method will be utilized on cracks generated from different tests, like fatigue and bending tests, and in different materials, such as metals. Conceptualization, A.K.D. and C.K.Y.L.; Methodology, A.K.D.; Software, A.K.D.; Formal analysis, C.K.Y.L.; Resources, C.K.Y.L.; Writing-original draft, C.K.Y.L. Writing-review & editing, C.K.Y.L.; Funding acquisition, A.K.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Startup Funds, Tsinghua University (7010100010), Guangdong Province (2024A11515010689), and Shenzhen Municipality (RCBS20231211090737067, QD2023018C, TD2024-05) Hong Kong RGC (16215018).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**CCD**} & \multicolumn{3}{c}{**DeepCrack**} \\ \cline{3-8}  & & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline Typical-RC-AI & 34M & 0.81 & 0.78 & 0.79 & 0.73 & 0.76 & 0.75 \\ DeeplabV3+ & 16M & 0.79 & 0.75 & 0.77 & 0.74 & 0.68 & 0.72 \\ FCN & 132M & 0.71 & 0.71 & 0.71 & 0.67 & 0.67 & 0.67 \\ CrackUnet & 32M & 0.91 & 0.86 & 0.88 & 0.87 & 0.83 & 0.86 \\ CrackSegnet & 15M & 0.85 & 0.87 & 0.86 & 0.82 & 0.79 & 0.82 \\
**Ours (SHSnet)** & **4M** & 0.95 & 0.96 & 0.95 & 0.96 & 0.93 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative performance of SHSnet on real-world dataset.