# A New Road Damage Detection Baseline with Attention Learning
## 2 Related Work
### Existing Road Damage Datasets  
For any deep learning algorithm, trainable data is the key. ADRD is no exception. In order to meet the needs of ADRD training, three road damage datasets have been released. The first one is the German Asphalt Pavement Distress (GAPs) dataset [25], which was collected from three different federal highways in Germany. It consists of 2468 gray-valued images with manual annotations. In practical applications, however, color images are more conducive to road damage detection than gray-scale images. The second dataset is the Pavement Image Dataset (PID) [26], which was captured by street cameras from 22 different road sections of the United States. It provides 7237 images and 67,469 labels. These labels are annotated as nine categories, but eight of them identify cracks, and the other is for potholes. This classification is not conducive to network training. The third dataset is the RDD dataset [3]. It was released in 2018 and has been updated to include three versions so far; it is the most widely used dataset in road damage detection tasks. This dataset was captured by a smartphone installed in a vehicle and contains 31,342 road damage annotations from India, Japan and the Czech Republic. However, the overall size of the images is relatively small due to the capture equipment. The damage density of the RDD dataset is low, meaning a large number of samples have only one damage bounding box. Rather than locating road damage, the dataset focuses more on the recognition of road damage.  
The datasets mentioned above have contributed to the development of ADRD. It is not surprising to find that most data are collected from developed countries. Due to various climates, road infrastructure standards and traffic rules, there are obvious differences in the data distributions of road damage among countries. In order to further promote the development of ADRD, this paper releases a new road damage dataset named CNRDD. In the process of data collection and labeling, types of damage are classified into eight categories according to the latest evaluation standards for highway technical conditions (JTG5210-2018). Considering the advantages of deep learning in recognition and classification, samples in which a single image contains multiple types of damage are selected to increase the difficulty and practical value of the dataset. In addition, in order to better access future intelligent road maintenance system, a professional onboard camera is chosen as the acquisition device. In this way, when road maintenance vehicles or driverless cars patrol daily in the future, they can provide timely feedback via uploading collected images to the cloud or server through 5G technology. Then, the ARDR algorithm can distinguish whether there is damage in the feedback images and give maintenance warnings for serious segments. This mode of ADRD can improve maintenance efficiency, reduce the economic cost caused by untimely maintenance and can reduce the number of traffic accidents caused by road damage. This work hopes to attract more researcher attention to ADRD and to create a new paradigm for the realization of intelligent road maintenance as soon as possible.