# A New Road Damage Detection Baseline with Attention Learning
## 1 Introduction  
Road damage detection (RDD) is an important task in the field of traffic infrastructure and involves locating and classifying road damage [1, 2, 3, 4]. It can identify roads that need maintenance to reduce potential safety hazards. However, current RDD approaches still depend on manual inspection in most countries. This not only consumes a lot of time and labor, but it also cannot ensure that the damaged road sections are repaired in time. Targeting this problem, automated detection of road damage (ADRD) based on deep learning began to appear and gradually become an emerging task in computer vision [4, 5]. Its research goal is to realize automatic road damage detection and analysis so as to play an important role in intelligent traffic maintenance systems. Most of the existing methods use convolutional networks to learn classification features and have achieved significant performance improvement on public foreign datasets [3, 6]. When these methods are directly applied to actual road images in other countries, however, there is a landslide decline in accuracy. This is because there are differences in data distribution between training data and actual testing data. After all, different climates, road construction standards and traffic rules in every country lead to differences in damage appearance and susceptible locations. In machine learning, transfer learning [3] or semi-supervised training [5] can effectively alleviate the impact of the distribution gap between training and testing data on generalization. However, the premise is that there is insufficient target domain data for training. At present, many datasets of road damage are private and unpublished [7; 8], which greatly limits the exploration of ADRD. Therefore, the most important research motivation of this paper is to release a new road damage dataset with professional classification and of uniform size so as to promote the research of relevant algorithms. In order to realize this research motivation, this paper collected and annotated the first edition of the China Road Damage Detection Dataset (CNRDD) over the course of three months. It consists of 4319 high-definition road images and provides 21,552 road damage annotations. These annotations contain eight types of damage: crack, longitudinal crack, lateral crack, subsidence, rutting, pothole, looseness and strengthening. Each category of damage also contains three different degree labels, namely mild, moderate and severe. Some damage examples are shown in Figure 1.  
As shown in Figure 1, multiple types of damage can appear in the same image. Although they do not overlap, the features of each kind of damage are very close to those of healthy road surfaces. In many cases, it is difficult even for the human eye to correctly classify only based on the images taken by vehicle cameras without field investigation. These phenomena make ADRD very challenging. The top view, which is similar to the perspective of a remotely sensed image, can alleviate the above difficulties to some extent [6; 9]. Such images need to be taken manually or by an unmanned aerial vehicle (UAV), however, which  
Figure 1: An example image from CNRDD. Various colored bounding boxes represent different types of road damage. makes it impossible to quickly patrol large-scale, high-traffic road scenes. In order to effectively patrol multiple long-distance roads, it is also necessary to use onboard cameras that can feed back the collected images to the terminal in a timely manner. This can automatically warn of the damaged sections to be repaired and improve maintenance efficiency. Thus, using the ADRD algorithm to realize the automatic early warning of damaged sections can improve maintenance efficiency. The images taken by the onboard camera are different from remote sensing images. These onboard images have obvious perspective relationships, so it is impossible to use a similar network for accurate detection [10]. Of course, these images are also different from conventional photos with perspective changes.