# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 2 Methodology
### Model Effectiveness Experiment
#### 2.4.2 Model Evaluation  
(1) Evaluation results of the ODNMs  
The ODNM was evaluated using precision (\(p\)), recall (R) and harmonious mean (F1) as evaluation indicators, and the results are shown in Table 3. According to reference [55], the learning efficiency of the same neural network model based on different deep learning frameworks is different. To ensure the effectiveness of the training of the ODNM, the corresponding learning framework of the model was constructed with reference to [55].  
Table 3 showed that the Faster R-CNN based on Keras2.1.5 had a better F1-value and R-value of 76.00% and 71.37%, respectively, than the other three models. In SSD based  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Original Image**} & \multicolumn{4}{c}{**SSNMs**} \\ \cline{2-5}  & **U-Net** & **PSPNet** & **DeepLabV3+** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the SSNMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ODNMs** & **Learning Frameworks** & **F1 (\%)** & **\(p\) (\%)** & **R (\%)** \\ \hline Faster R-CNN & Keras2.1.5 & 76.00 & 80.53 & 71.37 \\ SSD & Keras2.1.5 & 67.00 & 82.76 & 56.47 \\ YOLOV4 & TensorFlow2.2.0 & 33.00 & 76.06 & 21.18 \\ YOLOV5 & TensorFlow2.2.0 & 67.00 & 87.50 & 54.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the ODNMs.  
on Keras2.1.5, the F1-value, _p_-value and R-value of the model accuracy evaluation result were 67.00%, 82.76% and 56.47%, respectively. In YOLOV4 based on TensorFlow2.2.0, the F1-value, _p_-value and R-value of the model accuracy evaluation results were the lowest at 33.00%, 76.06% and 21.18%, respectively. On the other hand, YOLOV5(x) based on TensorFlow2.2.0 had the highest _p_-value of 87.50% for the model accuracy evaluation results, and the F1-value and R-value were better than YOLOV4 with 67.00% and 54.90%, respectively. It could be seen that the overall evaluation index values of the Faster R-CNN model were higher and more stable, and the evaluation index values of the YOLOV5(x) model were slightly higher than those of the SSD and YOLOV4. In conclusion, Faster R-CNN and YOLOV5(x) had better learning ability for crack features and could be preferred as network models for coarse-fine combined object detection.  
Evaluation results of the SSNMs  
Table 4 shows the evaluation results of the SSNMs. Similarly, to guarantee the effectiveness of the training of the SSNMs, the corresponding learning framework of the model was built with reference to [55].  
From Table 4, the AC-value, _p_-value and R-value of the model accuracy evaluation results for U-Net based on PyTorch1.2.0 were the highest at 98.37%, 89.11% and 90.28%, respectively. For PSPNet based on TensorFlow2.2.0, the AC-value, _p_-value and R-value of the model accuracy evaluation results were the lowest at 97.86%, 85.62% and 87.86%, respectively. For DeepLabV3+ based on PyTorch1.2.0, the accuracy evaluation results were between the other two models, and its AC-value, _p_-value and R-value were 98.20%, 87.58% and 90.12%, respectively. It was observed that the evaluation index values of the U-Net were slightly higher than those of DeepLabV3+ and PSPNet. In summary, it can be concluded that the U-Net has the strongest learning ability for the crack features and could be preferred as a network model for fine segmentation.