# A New Road Damage Detection Baseline with Attention Learning
## 3 China Road Damage Detection Dataset
### Data Collection and Annotation  
The G303 road segment in China is selected to collect road damage data at this time. Compared with other pavement, G303 has a large number of trucks/cars passing every day. Further, the temperature differences of this road segment are large across the four seasons. These problems lead to higher road damage density in collected single images, and road damage detection carried out on the dataset is more challenging. Ultimately, the dataset included 4319 images with 21,552 annotations. These pictures were uniformly captured using professional onboard cameras. All image are 1600 \(\times\) 1200 pixels and wide-view perspective. According to the evaluation standard for highway technical conditions, the CNRDD provides eight types of damage labels, including crack, longitudinal crack, lateral crack, subsidence, rutting, looseness, pothole and strengthening. Visual examples of these types of damage are shown in Figure 2.  
As shown in Figure 2, (a) cracks, (b) longitudinal cracks and (c) lateral cracks are the most common types of damage on daily roads. Among these, cracks appear like crocodile skin, and their initial shape is multiple parallel longitudinal cracks along the tire track line. With the repeated rolling of vehicles, lateral and oblique cracks appear between parallel longitudinal cracks to form cracks. (d) Subsidence refers to local depression of the road surface caused by foundation settlement. (e) Rutting is longitudinal depression of the road surface along the wheel track and is usually caused by insufficient compaction and poor composition of the mixed material during construction. (f) Looseness mainly comes from asphalt aging. It has various appearances, such as coarse and fine aggregate loss, pitted surfaces and even surface peeling. (g) Potholes are usually caused by looseness, cracks, subsidence and other types of damage that are not repaired in time. Its classification features are relatively obvious. (h) Strengthening is not real road damage. It represents areas where the damaged pavement has been repaired. Due to the diversity of in road damage repair, strengthening has many forms of appearance. Therefore, the degree of strengthening is not classified in the CNRDD dataset.  
For the above-mentioned road damage, Labellmg software based on Python is used for strict annotation. The personnel responsible for labeling have received professional  
Figure 2: Schematic diagram of each damage.  
training and can scientifically distinguish road damage. While the damage classification and bounding box are labeled, the degree of each damage is also finely classified (excluding strengthening). The annotation information of each image is saved in XML format, and the corresponding relationship between the classification code and the damage name is shown in Figure 3.  
As shown in Figure 3, the image name, damage type and bounding box position are provided in XML files. The 'degress' means the severity of damage: 1, 2 and 3 correspond to mild, moderate and severe, respectively. Moreover, 113 uncertain types of damage annotation are included in the CNRDD dataset, which facilitates users to exploit data flexibly. The CNRDD dataset provides 21,552 labels for 4319 images, including 3022 images in the training set and 1273 images in the test set, and 24 images do not contain any road damage. Detailed damage annotation statistics are displayed in Table 1.  
As shown in Table 1, the training set contains 15,085 annotations and the test set contains 6467 annotations. Both of them cover eight types of damage. However, the total number of four types of damage is less than 1000, which forms an obvious data imbalance with the other types of damage with mass annotations. In the training process, data augmentation may be considered to alleviate the negative impact of data imbalance on model generalization.