# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 4 Conclusions  
This study investigated automated crack detection based on a CNN. An open-source image dataset with two subsequent classes (no-crack and crack) was used. The image datasets were divided into 70%, 10%, and 20% for training, validation, and testing, respectively. The CNN model was designed based on AlexNet for image classification. AlexNet consists of convolution layers, pooling layers, fully connected layers, and SoftMax layers, as well as other operations such as ReLU, cross-channel normalization, and dropout layers. The last three layers (fully connected, SoftMax, and classification output layer) of the pretrained network were fine-tuned by transfer learning for the two classes (no-crack and crack). Image augmentations were then used to automatically resize the training images. The fine-tuned AlexNet model was trained by stochastic gradient descent with momentum (SGDM) optimizer. After training, the validation images and test images were classified using the fine-tuned network.  
The fine-tuned AlexNet model was trained, and the training progress evaluated the accuracy and cross-entropy loss for each epoch. The accuracy obtained at the 0.0001 learning rate and epoch 6 was 99.9%, and the validation loss was 0.1%. The trained model was validated, and it accurately predicted 1998 from 2000 images. The accuracy obtained during the validation was 99%, and the loss of accurate prediction was 0.1%. After the validation, the test images were classified using the trained network. In the test images, the  
Figure 10: Confusion matrices of the cross-dataset images: (**a**) bridge-decks; (**b**) pavements; (**c**) walls.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Dataset** & **Precision** & **Recall** & **F\({}_{1}\) Score** & **Accuracy** \\ \hline Bridge-decks & 0.89 & 0.91 & 0.90 & 0.84 \\ Pavements & 0.92 & 0.92 & 0.92 & 0.89 \\ Walls & 0.88 & 0.82 & 0.85 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics of cross-dataset test results.  
trained model accurately predicted 3998 from the total 4000 images. Considering the total test images, the prediction accuracy was 99.9% with 0.1% loss. This study confirmed that the CNN-based method demonstrates a high level of applicability to detect cracks, with a 99.9% accuracy. The performance of the trained model was quantified by the precision, recall, accuracy, and F\({}_{1}\) metrics, which were all equal to 0.99. Furthermore, the accuracies were compared to other pretrained DCNNs. AlexNet showed an accuracy of 0.99, which is beneficial for detecting and classifying cracks with high precision. The trained AlexNet model was further tested with different cross-dataset images which consisted of several obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. The existence of these obstructions resulted in a nominal loss of accurate predictions, with an accuracy of around 0.81-0.89%.  
Conceptualization, methodology, S.-T.K. and R.-S.R.; software, R.-S.R.; validation, formal analysis, S.-T.K.; writing--original draft preparation, review, and editing, S.-T.K. and R.-S.R. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study did not report any data.  
This research was supported by the Daegu University Research Grant (No. 20200222).  
The authors declare no conflict of interest.