# Crack Detection of Concrete Based on Improved CenterNet Model
## 4 Experiment and Result Analysis
### Training Process and Experimental Results  
To ensure the real and effective results of the comparative experiments, the training parameters used in all the experiments involved in this study were completely consistent. The initial learning rate of the training was 0.0001. The cosine annealing learning rate adjustment method was adopted, and the minimum learning rate was 0.00001. The batch size was set to eight during the training process. A total of 300 epochs were trained using the SGD optimization algorithm. The training experiments were conducted in five groups: original CenterNet with the backbone network of ResNet18, CenterNet with the channel space attention mechanism, CenterNet with the feature selection module, CenterNet with target size loss improvement, and CenterNet with the above three improvements. Table 2 compares the performance of CenterNet with the addition of CBAM and feature-selection modules, including FLOPS, FPS, and video memory. In the data training process, owing to the different difficulties involved in data feature extraction, there are overlaps and omissions in some data, as shown in Figure 8. Given this situation, the optimized model used in this study adopts the method of strengthening the feature extraction. This situation changed significantly after adding the feature extraction module, and the data processing accuracy was effectively improved. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network** & **FLOPs** & \
\begin{tabular}{l} **Memory** \\ **Footprint/MB** \\ \end{tabular} & **FPS** &
\begin{tabular}{l} **Video** \\ **Memory/MB** \\ \end{tabular} \\ \hline CenterNet & 13.06 & 50.3 & 296.5 & 1347 \\ CenterNet-CBAM & 13.06 & 51.7 & 189.9 & 1349 \\ CenterNet-FS & 16.35 & 51.1 & 250.2 & 1405 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of network performance before and after CenterNet optimization. Figure 7: Random transformation used in training. When the test environment of the controlled experiment was the same as that of the training environment, the batch size of the experiment was set to one. The ablation experiments are summarized in Table 3. From the ablation experiment, the following results were obtained:  
After the CBAM module was added, the model size increased by 1.4 MB, FPS decreased by 106.6, video memory increased by 2 MB, FLOPs remained unchanged, and AP increased by 0.072 compared with the original model. After adding the feature selection module, the model size increased by 0.8 MB, FPS decreased by 46.3, video memory increased by 58 MB, FLOPs increased by 3.29, and AP increased by 0.101 compared with the original model. After IOU optimization in the original model, the size increased by 0.5 MB, FPS decreased by 123.7, video memory increased by 31 MB, FLOPs increased by 2.2, and AP increased by 0.021 compared with the original model. Figure 8: Detection detail. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} & **CenterNet** & **CBAM** & **FS** & **Iou** & \
\begin{tabular}{c} **Memory** \\ **Foot-** \\ **print/MB** \\ \end{tabular} & **FPS** &
\begin{tabular}{c} **Video** \\ **Memory-** \\ **ory/MB** \\ \end{tabular} & **FLOPs** & **AP** \\ \hline
1 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 50.3 & 296.5 & 1347 & 13.06 & 0.751 \\
2 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 51.7 & 189.9 & 1349 & 13.06 & 0.823 \\
3 & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 51.1 & 250.2 & 1405 & 16.35 & 0.852 \\
4 & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 50.8 & 172.8 & 1378 & 15.26 & 0.772 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 52.3 & 237.1 & 1382 & 15.43 & 0.864 \\
6 & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 51.8 & 196.8 & 1375 & 16.49 & 0.872 \\
7 & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & 51.9 & 253.7 & 1401 & 16.02 & 0.869 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 52.4 & 270.9 & 1409 & 16.87 & 0.905 \\ \hline \hline \end{tabular}
\end{table}