# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 5 Conclusions  
This study proposed a two-stage deep learning framework that integrates GoogleNet for crack detection and SegNet for semantic segmentation on cultural heritage surfaces. In contrast to many existing approaches that rely exclusively on end-to-end segmentation models, the proposed method introduces a modular, lightweight, and interpretable pipeline tailored to the practical constraints of heritage inspection.  
In addition to demonstrating robust performance across varying wall textures, the study emphasizes the impact of surface characteristics, such as mottling and brick geometry, on segmentation accuracy. The application of Gray-Level Co-occurrence Matrix (GLCM)-based texture analysis further enhances the interpretability of model performance across different wall types.  
Moreover, the incorporation of a dual-laser projection system provides a straightforward and effective means of converting pixel-based predictions into real-world crack length measurements. This practical enhancement facilitates the integration of deep learning methodologies into conservation workflows.  
While SegNet offers a favorable trade-off between accuracy and computational efficiency, comparative analysis with results from the literature suggests that more advanced architectures, such as DeepLabv3+ or Mask R-CNN, may achieve higher segmentation accuracy. However, such improvements would likely come at the cost of increased model complexity and computational demand. Consequently, model selection should be guided by the specific requirements of the task, available resources, and the nature of the input data.  
Conceptualization, W.-C.L. and W.-C.H.; methodology, W.-C.H. and Y.-S.L.; software, W.-C.H. and Y.-S.L.; validation, W.-C.L., W.-C.H., Y.-S.L. and H.-M.L.; formal analysis, W.-C.H. and Y.-S.L.; investigation, W.-C.L. and W.-C.H.; resources, W.-C.L.; data curation, W.-C.H.,Y.-S.L. and H.-M.-L.; writing--original draft preparation, W.-C.L. and Y.-S.L.; writing--review and editing, W.-C.L. and H.-M.L.; visualization, Y.-S.L. and H.-M.L.; supervision, W.-C.L.; project administration, W.-C.L. and W.-C.H.; funding acquisition, W.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council, Taiwan, grant number 111-2625-M-239-001.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This research was partially funded by the National Science and Technology Council, Taiwan, under grant number 111-2625-M-239-001. We gratefully acknowledge this financial support.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CL & Crack Length \\ CLI & Crack Length in Image \\ CNNs & Convolutional Neural Networks \\ DA & Data Augmentation \\ DLP & Distance between Laser Points \\ DLPI & Distance between Laser Points in Image \\ FCNs & Fully Convolutional Networks \\ FN & False Negative \\ FP & False Positive \\ GLCM & Gray-Level Co-occurrence Matrix \\ ILSVRC & ImageNet Large Scale Visual Recognition Challenge \\ IoU & Intersection over Union \\ NiN & Network in Network \\ ReLU & Rectified Linear Unit \\ R-CNN & Region-based Convolutional Neural Networks \\ SGDM & Stochastic Gradient Descent with Momentum \\ TN & True Negative \\ TP & True Positive \\ UAVs & Unmanned Aerial Vehicles \\ YOLO & You Only Look Once \\ \end{tabular}