# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 3 Results
### Experimental Data
#### 3.2.2 Refined Model Dataset  
Since there is little related research on the identification of surface cracks in soil and rock masses, the datasets of surface cracks in soil and rock masses required for the construction of the refined model cannot be directly obtained. Thus, we need to produce the dataset of surface cracks in soil and rock masses. In this paper, we collect, sort, and crop the crack images in soil and rock masses and produce a smaller dataset for the identification of cracks in soil and rock masses. The dataset has a total of 400 images, and the size of each image is adjusted to the same size as the original dataset: \(227\times 227\) pixels. Among them, 200 are images contain soil and rock masses with surface cracks, and 200 are images contain soil and rock masses without surface cracks. Eighty percent of them are randomly selected as the training set of the rock and soil crack recognition dataset, and twenty percent are selected as the test set. Therefore, there are a total of 320 images in the training set, 160 images with cracks, and 160 images without cracks. There are a total of 80 images in the test set, 40 images with cracks and 40 images without cracks. Some images of the dataset are illustrated in Figures 8 and 9.  
\begin{table}
\begin{tabular}{l l} \hline
**Software** & **Details** \\ \hline OS & Windows 10 Professional \\ Programming language & Python \\ Deep learning framework & PyTorch \\ Dependent library & Torch, Torchvision, CUDA, PIL etc. \\ \hline \end{tabular}
\end{table}
Table 1: Software environment configurations used in this paper.  
\begin{table}
\begin{tabular}{l l} \hline
**Hardware** & **Details** \\ \hline CPU & Intel Xeon Gold 5118 CPU \\ CPU Frequency (GHz) & 2.30 \\ CPU core & 48 \\ CPU RAM (GB) & 128 \\ GPU & Quadro P6000 \\ GPU memory (GB) & 24 \\ CUDA cores & 3840 \\ CUDA version & v9.0 \\ \hline \end{tabular}
\end{table}
Table 2: Hardware environment configurations used in this paper.  
Figure 6: The concrete crack dataset that contains the images of the cracks.  
Figure 7: The concrete crack dataset that does not contain the images of the cracks.  
Figure 8: The dataset of surface cracks of soil and rock masses that contains the images of the cracks.  
Figure 9: The dataset of surface cracks of soil and rock masses that does not contain the images of the cracks.  
Test Results and Analysis of the Pretrained Deep Learning Models for the Identification of Slope Cracks  
The deep neural networks used in this paper are LeNet5, AlexNet, the LeNet5 network model improved by InceptionV3's InceptionA and InceptionE modules (referred to as InceptionA and InceptionE in the following for easy identification), ResNet18, MobileNet, and Vision Transformer. The loss function used in the model is \(torch.nn.CrossEntropyLoss()\), and the optimizer is \(torch.optim.SGD()\). The comparison and analysis of each model in terms of accuracy and efficiency are as follows. The statistics of the total number of parameters and floating point operations for each model are listed in Table 3.  
As can be seen from Table 3, the floating point operations and parameters of LeNet5 are very small, indicating a simple model structure and a small memory footprint. In contrast, the floating point operations and parameters of InceptionE are very large, which indicates that the model structure is complex and occupies more memory. Among the other models, the floating point operations of MobileNet, ResNet18, InceptionA, AlexNet, and Vision Transformer increase in order. Furthermore, the parameters are AlexNet, MobileNet, InceptionA, ResNet18, and VisionTransformer in descending order. The accuracy and GPU parallel computation time of each model are compared and analyzed as follows.