## 6 Verification of Blast Hole Digit Recognition Method Applied in Tunnels
### Discussion  
As tunnel construction projects grow increasingly complex, the demand for highly accurate and efficient detection methods continues to escalate. Recent advancements in integrating 3D laser scanning with artificial intelligence and UAV measurement technologies offer promising avenues to address these needs [63] For instance, [PERSON] et al. proposed a 3D reconstruction method for tunnel surfaces based on point cloud data, specifically addressing issues of high data volume and redundancy that often complicate surface reconstruction [64]. Similarly, [PERSON] and colleagues emphasized the critical role of precision in data acquisition, introducing a method that combines 3D laser scanning with blast-site data to assess the impact on halfhole structures, further underlining the need for exacting detail in post-blast assessments [65]. Despite the effectiveness of technologies like 3D laser scanners and UAVs, our approach offers substantial cost savings without compromising accuracy [66]. The deployment of advanced digital imaging technologies, particularly high-resolution cameras and precision scanners, significantly enhances post-blast assessments by capturing high-detail visual data [67]. Through cutting-edge photographic and scanning techniques, this data enables an unprecedented depth of analysis. Specialized 3D reconstruction software, such as Matlab 2024a, CloudCompare2.13.2, and COLMAP 3.5, processes the data rapidly, delivering \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Lithology** & \multicolumn{4}{c}{**Half Porosity (\%)**} \\ \hline  & Favorable & Moderate & Ordinary & Differ \\ hard rock & \textgreater{}85 & 70–85 & 50–70 & \textless{}50 \\ medium & \textgreater{}70 & 50–70 & 30–50 & \textless{}30 \\ limestone & \textgreater{}50 & 30–50 & 20–30 & \textless{}20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality criteria for evaluating half porosity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**HalfHole** & **Recognized Length** & **Design Value Length** & **SemiPorosity** \\
**Numbering** & **(m)** & **(m)** & **(m)** & **SemiPorosity** \\ \hline
1 & 0.98 & 2 & 49.0\% \\
2 & 1.87 & 2 & 93.5\% \\
3 & 1.03 & 2 & 51.5\% \\
4 & 1.12 & 2 & 56.0\% \\
5 & 1.05 & 2 & 52.5\% \\
6 & 0.99 & 2 & 49.5\% \\ add up the total & 7.04 & 12 & 58.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identification table of halfhole point cloud data. precise visualizations of post-blast conditions and enabling immediate acquisition of blast-depth measurements. Such efficiency is vital in modern tunnel construction, where timely and accurate data underpin the optimization of blast parameters, operational continuity, and project efficiency. The ability to make data-driven adjustments based on these insights is invaluable in enhancing both the efficacy and quality of tunnel blasting. Real-time adjustments informed by comprehensive data play a pivotal role not only in immediate operational decisions but also in broader structural design, safety protocols, and long-term project planning. By delivering a holistic evaluation of blast quality from multiple perspectives, this method ensures that tunneling projects adhere to rigorous standards of safety and efficiency. While this study focuses on road tunnels, the findings are directly transferable to mining operations, such as open-pit blasting, underscoring the method's adaptability across varied geological conditions and its practical value for projects with unique structural and environmental challenges. However, the precision of these methods remains sensitive to environmental factors, such as dust and suboptimal lighting--conditions that are often prevalent in tunnel settings. Future research should prioritize refining these technologies to better handle such challenges, potentially through advanced filtering algorithms and calibration techniques that can mitigate the impacts of environmental variables on data quality.