# Crack Detection of Concrete Based on Improved CenterNet Model
## 4 Experiment and Result Analysis
### Training Process and Experimental Results

Table 3: CenterNet optimized process ablation experiment. After the addition of the feature selection module, the optimized model decreased the target size loss faster than the original CenterNet because the feature selection module could adaptively select the underlying features (such as the target texture and edge information) in the downsampling process and add them to the feature map in the upsampling process. Thus, the target size could be learned more quickly. The change curve for the CenterNet target size loss after the original CenterNet and the addition of the feature selection module are shown in Figure 9.  
The feature selection module can adapt to underlying features, which is also evident in the actual detection effect. As shown in Figure 10, after adding the feature selection module, the optimized model can predict the crack size more accurately due to the inclusion of information such as the crack edge. The CBAM and feature selection modules, particularly the CBAM module, significantly impact the reasoning speed of the network. This is because, after the CBAM module is added to each ResBlock, the FPS of the network decreases overall, whereas the feature  
Figure 10: Comparison of detection results between CenterNet and feature selection module. Figure 9: Loss curve of network target size between CenterNet and feature selection module. selection module reduces the FPS. Regarding video memory usage, the impact of the two additional modules was relatively small. The feature information of the entire network is compressed by the subsampling module, which reduces the workload of subsequent network training and increases the reasoning speed of the entire network. The input information in the upper layer of the network is enhanced after the feature extraction module, and the upsampling stage uses fewer convolutional layers to improve the running speed of the network. The information about each input and output layer of the overall network optimized in this study is shown in Table 4.  
To demonstrate the improvement in the performance of the model before and after optimization more intuitively, five groups of training processes were randomly selected for comparison, as shown in Figure 11. Dark blue represents the data processing accuracy of the original CenterNet model, and yellow represents the improvement in accuracy brought about by the optimized CenterNet-CBAM-FS-IOU model. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Net** & **Input Size** & **Input Channel** & **Output Size** & **Output Channel** \\ \hline Convolution 1 & \(512\times 512\) & 3 & \(128\times 128\) & 64 \\ Res-Block1 & \(128\times 128\) & 64 & \(128\times 128\) & 64 \\ CBAM1 & \(128\times 128\) & 64 & \(128\times 128\) & 64 \\ Res-Block2 & \(128\times 128\) & 64 & \(64\times 64\) & 128 \\ CBAM2 & \(64\times 64\) & 128 & \(64\times 64\) & 128 \\ Res-Block3 & \(64\times 64\) & 128 & \(32\times 32\) & 256 \\ CBAM3 & \(32\times 32\) & 256 & \(32\times 32\) & 256 \\ Res-Block4 & \(32\times 32\) & 256 & \(16\times 16\) & 512 \\ CBAM4 & \(16\times 16\) & 512 & \(16\times 16\) & 512 \\ Upper sampling layer 1 & \(16\times 16\) & 512 & \(32\times 32\) & 256 \\ Upper sampling layer 2 & \(32\times 32\) & 256 & \(64\times 64\) & 128 \\ Upper sampling layer 3 & \(64\times 64\) & 128 & \(128\times 128\) & 64 \\ Target center point & \(128\times 128\) & 64 & \(128\times 128\) & 1 \\ The target center is biased & \(128\times 128\) & 64 & \(128\times 128\) & 2 \\ Target size & \(128\times 128\) & 64 & \(128\times 128\) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CenterNet improved network layer input and output. Figure 11: Performance improvement after CenterNet optimization demonstration. After optimization, the overall processing accuracy of CenterNet improved to a certain extent, and it could effectively identify cracks in construction concrete with a shorter training time. The actual detection effect is shown in Figure 12, where the red box represents the detection crack prompts, and the number represents the detection number.