# A New Road Damage Detection Baseline with Attention Learning
## 3 China Road Damage Detection Dataset
### Evaluation Measures  
The evaluation system of the CNRDD dataset is the same as other detection datasets. Precision, Recall, [EMAIL_ADDRESS], and F1-Score are used as evaluation indicators. Precision refers  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Name** & **Crack** & **Longitudinal Crack** & **Lateral Crack** & **Subsidence** & **Ruting** & **Pothole** & **Looseness** & **Strengthening** & **Uncertain** & **Total** \\ \hline train & 1816 & 3747 & 4141 & 955 & 3412 & 320 & 490 & 129 & 75 & 15,085 \\ test & 771 & 1631 & 1733 & 438 & 1433 & 155 & 220 & 48 & 38 & 6467 \\ sum & 2587 & 5378 & 5874 & 1393 & 4845 & 475 & 710 & 177 & 113 & 21,552 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mathematical statistics of different types of damage in training set and test set.  
Figure 3: Description of road damage annotation. (**a**) Screenshot of annotation file. (**b**) Correspondence between classification codes and damage name.  
to the number of correct prediction results divided by the total number of predictions. It is related to true positives (TP) and false positives (FP) and is defined in Equation (1). Recall indicates the correctly predicted proportion in all annotation samples, which is related to true positives (TP) and false negatives (FN). It can be calculated by Equation (2).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
where TP, FN and FP are related to Intersection over Union (IoU). Between the predicted box and the ground-truth (GT) box, IoU calculates the quotient of the overlap area and the union area. When the IoU between the predicted bounding box and GT exceeds 0.5, the predicted instance is considered as a TP. When a predicted box has less than 0.5 IoU overlap with the GT, it is categorized as a FP. In addition, if the predicted result is not in GT at all or the predicted damage label is different from the GT label, the result is counted as an FP. FN indicates the number of road damage instances that the model fails to predict or mispredicts. According to these rules, Precision and Recall can be calculated by Equations (1) and (2), respectively. This concept can be better explained by Figure 4.  
In Figure 4, each solid white diamond represents a positive sample, each hollow white diamond represents a negative sample, and diamonds in the black solid line represent the instances predicted as positive samples by the model. As shown in Figure 4, TP (green area) represents the positive samples predicted as positive samples, FP (yellow area) represents the negative samples predicted as positive samples, and FN (light grey area) represents the positive samples predicted as negative samples.  
However, Recall and Precision calculated according to Equations (1) and (2) are in a relationship based on ebb and flow. When Recall is improved, Precision is bound to decrease. In order to better evaluate the model performance on the CNRDD dataset, the F1-Score, defined in Equation (3), and the [EMAIL_ADDRESS] need be introduced.  
\[\text{F}_{1}=2\times\frac{(\text{Precision}\times\text{Recall})}{(\text{ Precision}+\text{Recall})} \tag{3}\]  
Figure 4: Description of Precision and Recall.  
In Equation (3), F1 refers to the F1-Score. It is the harmonic average of Precision and Recall and can represent the average accuracy of a network to detect road damage. The goal of training networks is to maximize the F1-Score and obtain the optimal balance between Precision and Recall. The [EMAIL_ADDRESS] represents the mean average precision when the IoU is set to 0.5. In a Precision-Recall Curve (PRC) where Recall is the abscissa and Precision is the ordinate, the area under the PRC is recorded as the [EMAIL_ADDRESS]. The mean of all categories of the [EMAIL_ADDRESS] is the [EMAIL_ADDRESS]. It can measure the detection performance of the network for different types of road damage.