# A New Road Damage Detection Baseline with Attention Learning
## 5 Experiment and Analysis
### Comparative Analysis of Different Datasets  
The RDD2020 dataset is the most important public dataset for current damage detection tasks. It provides two test sets without GT and a training set with four types of road damage \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Value** & **Name** & **Value** \\ \hline epoch, f & 420 & epoch,b & 100 \\ lr, f & 0.01 & lr,b & 0.005 \\ canny\_L & 30 & canny\_H & 170 \\ batch size & 25 & momentum & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Implementation details of training and fine-tuning. labels (longitudinal cracks, reverse cracks, complex cracks and potholes). On this dataset and the self-collected CNRDD dataset, we carry out experimental analysis on multiple state-of-the-art algorithms [4, 6, 9, 12, 15, 18, 34, 35, 36, 37, 38, 39, 40, 41, 42]. Firstly, we compare the F1-Scores of different models on the same dataset to evaluate the proposed method. Secondly, we investigate the detection difficulty of the CNRDD dataset by comparing it with the RDD2020 dataset. The experimental results are shown in Tables 4 and 5. In Table 4, Test1 Score and Test2 Score represent the F1-Scores on two test sets of RDD2020. All the comparative data come from the evaluation system of the Global Road Damage Detection Challenge (GRDDC) based on RDD2020 ([[https://rdd2020.sekilab.global/submissions/](https://rdd2020.sekilab.global/submissions/)]([https://rdd2020.sekilab.global/submissions/](https://rdd2020.sekilab.global/submissions/)) (accessed on 10 December 2021)). The term 'Ensemble' indicates that the algorithm uses ensemble learning, and 'Single' represents that the algorithm is a single network without ensemble learning. Our proposed method is classified as a single network. As shown in Table 4, our Test1 Score and Test2 Score are 0.614 and 0.6083, respectively. The proposed method achieves the best performance of the single methods. Of course, it is not difficult to find that the accuracy of an ensemble learning method is generally higher than that of single methods. ISMC [34] explores Faster RCNN and YOLO v5 at the same time. SIS [34] adopts YOLO v4 with different resolution. DD-VISION [36] integrates 18 models including Fast RCNN, ResNext, HR Net, CBNet, Resnet, etc. These ensemble learning methods improve the accuracy of road damage detection by integrating different features, but they also bring a huge cost of training resources. In Table 5, the experimental results of YOLO v5 [15], Faster R-CNN [12], fcos [18] and Dongjuns [41] are obtained by retraining with their open-source codes on the CNRDD dataset. Data are from the average of 10 repeated experiments. Although the proposed method takes YOLO v5 as the baseline, it is improved by introducing an attention fusion module and salient feature learning with NAM. Comparing the experimental data in Table 5, it can be seen that the proposed method has achieved significant performance improvement on the CNRDD dataset. The F1-Score can reach 34.73%. This demonstrates that the introduction of attention fusion and normalization can effectively enhance the learning of salient regional features so as to improve the discrimination of damage features. After evaluating the effectiveness of the proposed method, we compare the performance of Dongjuns [41] with the proposed method in Tables 4 and 5. We find that the F1-Score of the same model on the RDD2020 dataset is better than the performance on the CNRDD dataset. This is mainly because the classification number and damage density of the two datasets are different. The classification of the CNRDD dataset is more comprehensive,