# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Comparison with Other Deep Learning Models  
In deep learning, model performance is often related to the number of parameters, which represent trainable weights and biases. The GoogleNet classification model contains approximately 6.8 million parameters, while the SegNet segmentation model has about 14.7 million parameters; the MATLAB implementation of SegNet includes approximately 6 million. These represent significant reductions compared to earlier models such as AlexNet (\(\sim\)60 million parameters) for classification and VGG16 (\(\sim\)134 million) for segmentation. However, compared to more recent lightweight architectures designed for mobile  
Figure 24: GLCM results for different image categories.  
deployment, such as MobileNetV3-BLS (\(\sim\)6 million parameters) for classification and ENet (\(\sim\)0.4 million) for segmentation, GoogleNet and SegNet are relatively larger.  
The MobileNetV3-BLS model has achieved strong performance in classification tasks, with reported accuracy of 98.9%, precision of 100%, recall of 93.8%, and an F1-score of 0.968 when trained on a dataset of over 6000 images [57]. ENet, applied to wall surface segmentation tasks including cracks, efflorescence, rebar exposure, and spalling, has achieved an IoU of 0.45 for crack segmentation [58]. While MobileNetV3-BLS performs comparably to GoogleNet in classification, this is expected as classification only requires identifying the presence of a class within an image. In contrast, semantic segmentation requires pixel-wise classification, making it inherently more complex; consequently, models with a higher parameter count generally yield better performance in this domain.  
One-stage object detection models such as YOLO v5 and YOLO v7, which range from a few million to tens of millions of parameters depending on the variant, are capable of high detection accuracy and rapid inference [59; 60]. Although these models can provide bounding boxes for crack localization, SegNet directly outputs pixel-level location and extent of cracks via semantic segmentation. Combining object detection and segmentation may thus introduce redundant computation without clear added value.  
Despite its moderate parameter count, SegNet typically underperforms compared to more advanced segmentation architectures. For instance, DeepLabv3+ has demonstrated an IoU exceeding 0.9 in crack segmentation tasks [61], significantly surpassing SegNet. These gains are attributed to architectural innovations such as atrous convolutions, skip connections, and multi-scale context aggregation [62]. However, such models come with trade-offs: DeepLabv3+ typically includes over 40 million parameters and incurs higher computational costs and slower inference speeds [62].  
Two-stage models like Faster R-CNN and Mask R-CNN can also achieve high detection accuracy (\(\sim\)90%) when trained on sufficient data [33], but they demand substantial computational resources. For example, Mask R-CNN requires over ten times the training and validation time of SegNet on the same dataset. Additionally, with limited data, such models are more prone to overfitting. Mask R-CNN, which performs both detection and segmentation, generally exceeds 40 million parameters.  
In summary, this study proposes a two-stage deep learning framework integrating GoogleNet for crack classification and SegNet for semantic segmentation. This approach balances computational efficiency with segmentation precision, offering a lightweight and practical solution for large-scale inspections of cultural heritage structures. The incorporation of dual-laser-based scale calibration further enables automated and field-applicable crack length estimation. While SegNet fulfills the needs of this study, future work may consider replacing it with higher-performing models such as DeepLabv3+ when segmentation accuracy is of utmost priority, acknowledging the trade-off in model complexity and computational demand.