# An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges
## 4 Model Construction and Experiments
### Experimental Steps  
The parameters of the pre-trained VGG-16 model were migrated to the surface defect detection model for cement concrete bridges, which follows the principles of fine tuning and transfer learning. In addition, the fully connected layers and Softmax classification layer of the VGG-16 model were adjusted. Our experiments were carried out in the following steps.  
Step 1. Inputting the surface defect samples of cement concrete bridges.  
The images of the seven defect classes were randomly extracted from the database of defect samples of cement concrete bridges, and inputted as the training set.  
Step 2. Preprocessing the input images.  
The images are polluted by noises in collection and transfer. In this case, the morphology-based weight adaptive denoising method is applied to enhance the image features and eliminate the useless noises from the images. Then, the input images were scaled and cropped into the standard size of 224x*224.  
Step 3. Constructing the surface defect model.  
The multi-class logistic regression objectives were optimized through backpropagation with small batch gradient descent with momentum (Batch size: 256. Momentum: 0.9). To void overfitting, the  
Figure 9: Structure of our visual geometry group network-16 (VGG-16) model.  
weight attenuation was regularized (L2 penalty factor: \(5\times 10^{4}\)) and the first two fully connected layers were discarded (discard ratio: 0.5). The learning rate was initialized as \(10^{-2}\), and then reduced by 10 times when the accuracy of the test set no longer improved.  
Step 4. Adjusting the VGG-16 model  
Based on the pre-trained VGG-16 model, the three fully connected layers were reduced to two and the Softmax classifier with 1,000 tags was replaced with a Softmax classification layer with seven tags.  
Step 5. Fine tuning and transfer learning  
The pre-trained parameters of the VGG-16 were migrated to the 13 convolutional layers and pooling layers of the surface defect detection model for cement concrete bridges through fine tuning and transfer learning.  
Step 6. Training the surface defect detection model  
Before training, the momentum, learning rate, and training time were configured manually. The parameters of the first 13 convolutional and pooling layers were kept constant, and the model was randomly initialized to obtain the initial loss. The model was then iteratively optimized through error backpropagation and gradient descent, which aims to train the parameters of the subsequent two fully connected layers and the Softmax classification layer with seven tags. The optimization was terminated until the value of the loss function reached the minimum. At the termination, the weights and biases of the model were optimal, which correspond to the best modelling effect.  
Step 7. Testing the surface defect detection model  
The remaining images of the seven defect classes were extracted from the database and used to verify the established surface defect detection model. The accuracy and efficiency of the model were tested through the steps in Figure 10.  
Figure 10: Experimental flow.