# A New Road Damage Detection Baseline with Attention Learning
## 5 Experiment and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Test1 Score** & **Test2 Score** & **Solution** \\ \hline SUITC [42] & 0.5636 & 0.5707 & Ensemble \\ Dongjuns [41] & 0.5683 & 0.5710 & Ensemble \\ DD-VISION [36] & 0.629 & 0.6219 & Ensemble \\ SIS Lab [35] & 0.6275 & 0.6358 & Ensemble \\ IMSC [34] & **0.6748** & **0.6662** & Ensemble \\ \hline titan.mm [9] & 0.5814 & 0.5751 & Single \\ RICS [4] & 0.565 & 0.547 & Single \\ AIRS-CSR[6] & 0.554 & 0.541 & Single \\ CSIT [37] & 0.5413 & 0.5430 & Single \\ BDASL [38] & 0.5368 & 0.5426 & Single \\ IDVI [39] & 0.51 & 0.514 & Single \\ E-LAB [40] & 0.472 & 0.4656 & Single \\
**Ours** & **0.614** & **0.6083** & Single \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison on RDD2020 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]** & **F1-Score** & **FLOPs** \\ \hline YOLO v5 [15] & 24.8 & 32.25 & **28.73G** \\ Faster R-CNN [12] & 23.8 & 24.1 & 292.28G \\ fcos [18] & 18.4 & 21.41 & 284.46G \\ Dongjuns [41] & 25.2 & 22.67 & 402.14G \\
**Ours** & **26.6** & **34.73** & 28.77G \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison on CNRDD dataset. and there is a degree category among same damage. The damage density of the CNRDD dataset is higher than that of the RDD2020 dataset, which is closer to the actual situation that road damage is easy to gather. Comparing damage identification with the RDD2020 dataset, therefore, the CNRDD dataset has more advantages in challenge and practicability. In addition to quantitative analysis, we also perform visualization analysis on different datasets. YOLO v5 [15], Faster R-CNN [12] and our proposed method are selected for comparison; the detection results are shown in Figure 7.  
In Figure 7, the first column is the raw image with the GT bounding box, and from the second to the fourth column are the detection results of YOLO v5 [15], Faster R-CNN [12] and the proposed method, respectively. The colored boxes represent the detection results for different types of damage. The comparison shows that the proposed method has better perception of damage location and a relatively lower missed detection rate. However, in addition to damage with obvious features, the existing methods (including the proposed method) still have serious problems of wrong detection and missing detection. This paper only provides an idea of exploiting attention learning for road damage detection. In the follow-up, many researchers need to work together to promote the development of ADRD. ### Comparative Analysis of Different Datasets  
In order to analyze the impact of attention fusion, salient feature learning and two-stage training on road damage detection, we also conduct ablation analysis on the CNRDD test set and simulation RDD2020 test set. The simulated test set consists of 20% of the data randomly selected from the RDD2020 training set. YOLO v5 [15] is the baseline of the proposed method. The improved components are added to the baseline in turn, and the experimental results are shown in Table 6.  
Figure 7: Visual comparative analysis of RDD2020 and CNRDD datasets. The first two samples are from the RDD2020 dataset, and the last three images are from the CNRDD dataset. Comparing the proposed method against the baseline in Table 6, AF improves F1-Score by 0.67% and 0.66% on the CNRDD and RDD2020 datasets, respectively. SFLwN increases the F1-Score by 1.72% and 0.54% on the CNRDD and RDD2020 datasets, respectively. This indicates that the two designed modules are able to improve the discrimination of damage features by enhancing the attention to salient areas. Interestingly, the result of introducing SFLwN alone is not as good as AF on the RDD2020 simulation test set. However, cooperation between the two components improves the F1-Score and [EMAIL_ADDRESS] significantly. In addition, the accuracy of the complete method is further improved through two-stage training.