## 6 Findings and Future Research Scope
### Findings of the Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Year** & **Title** & **Code Link** \\ \hline
[102] & 2019 & Fully convolutional networks for automatic pavement crack segmentation & [[https://github.com/RyM-CIC/Crack-segmentation](https://github.com/RyM-CIC/Crack-segmentation)]([https://github.com/RyM-CIC/Crack-segmentation](https://github.com/RyM-CIC/Crack-segmentation)) (accessed on 25 May 2024) \\
[103] & 2020 & Curvilinear structure anisotropy filtering and adaptive graph-cuts & [[https://github.com/[PERSON]/PCmPA-PCmIFA](https://github.com/[PERSON]/PCmPA-PCmIFA)]([https://github.com/[PERSON]/PCmPA-PCmIFA](https://github.com/[PERSON]/PCmPA-PCmIFA)) (accessed on 25 May 2024) \\
[104] & 2020 & Token based crack detection & [[https://github.com/[PERSON]/CrackTokenToolbox](https://github.com/[PERSON]/CrackTokenToolbox)]([https://github.com/[PERSON]/CrackTokenToolbox](https://github.com/[PERSON]/CrackTokenToolbox)) (accessed on 25 May 2024) \\
[26] & 2020 & Feature pyramid and hierarchical boosting network for pavement crack detection & [[https://github.com/fyangneil/pavement-crack-detection](https://github.com/fyangneil/pavement-crack-detection)]([https://github.com/fyangneil/pavement-crack-detection](https://github.com/fyangneil/pavement-crack-detection)) (accessed on 25 May 2024) \\
[89] & 2021 & DAUNet: deep augmented neural network for pavement crack segmentation & [[https://github.com/dvalex/daunet](https://github.com/dvalex/daunet)]([https://github.com/dvalex/daunet](https://github.com/dvalex/daunet)) (accessed on 25 May 2024) \\
[105] & 2021 & Attention-based convolutional neural network for pavement crack detection & [[https://github.com/wanhaifengytu/](https://github.com/wanhaifengytu/)]([https://github.com/wanhaifengytu/](https://github.com/wanhaifengytu/)) (accessed on 25 May 2024) \\
[106] & 2022 & A novel approach for detection of pavement crack and sealed crack using image processing and salp swarm algorithm optimized machine learning & [[https://github.com/NDHoangDTU/CV_SSA_SVM_Crack_SealedCrack](https://github.com/NDHoangDTU/CV_SSA_SVM_Crack_SealedCrack)]([https://github.com/NDHoangDTU/CV_SSA_SVM_Crack_SealedCrack](https://github.com/NDHoangDTU/CV_SSA_SVM_Crack_SealedCrack)) (accessed on 25 May 2024) \\
[65] & 2022 & An efficient method for detecting asphalt pavement cracks and sealed cracks based on a deep data-driven model & [[https://github.com/CHDyshli/PavementCrackDetection](https://github.com/CHDyshli/PavementCrackDetection)]([https://github.com/CHDyshli/PavementCrackDetection](https://github.com/CHDyshli/PavementCrackDetection)) (accessed on 25 May 2024) \\
[107] & 2023 & Multiscale attention networks for pavement defect detection & [[https://github.com/xtu502/pavement-defects](https://github.com/xtu502/pavement-defects)]([https://github.com/xtu502/pavement-defects](https://github.com/xtu502/pavement-defects)) (accessed on 25 May 2024) \\
[108] & 2023 & Computer vision-based recognition of pavement crack patterns using light gradient boosting machine, deep neural network, and convolutional neural network & [[https://github.com/NhatDucHoang/LightGBM_PaveCrackPatterns](https://github.com/NhatDucHoang/LightGBM_PaveCrackPatterns)]([https://github.com/NhatDucHoang/LightGBM_PaveCrackPatterns](https://github.com/NhatDucHoang/LightGBM_PaveCrackPatterns)) (accessed on 25 May 2024) \\
[76] & 2023 & Development of ai- and robotics-assisted automated pavement-crack-evaluation system Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network & [[https://github.com/Masrur02/AMSEL_robot](https://github.com/Masrur02/AMSEL_robot)]([https://github.com/Masrur02/AMSEL_robot](https://github.com/Masrur02/AMSEL_robot)) (accessed on 25 May 2024) \\ \hline \hline \end{tabular}
\end{table} 
Table 17: List of algorithms with disclosed codes, all accessed on 25 May 2024. * Countries with greater influence include China, the United States, Canada and the United Kingdom.