# A New Road Damage Detection Baseline with Attention Learning
## 2 Related Work
### Existing Damage Detection Approaches  
As we all know, object detection based on deep learning is the most successful task in the field of computer vision. Most of the existing methods can be divided into one-stage or two-stage according to the network structure [11; 12; 13; 17]. Two-stage methods consist of bounding box regions and proposal classification. This technique began with RCNN and then evolved into Faster RCNN through the introduction of a region proposal network and an ROI pooling layer [11; 12]. Faster RCNN is widely used in pedestrian detection, remote sensing image detection and other practical applications. With the support of pre-training models from the ImageNet dataset, this method can generally obtain good accuracy, but its complex structure leads to a large amount of time and memory space for training and testing. The one-stage method, represented by the you-only-look-once (YOLO) series, directly regards target detection as a regression problem [13; 14; 15; 17]. It can carry out end-to-end object detection in the whole image. The original YOLO v1 gradually evolved into YOLO v5 by introducing batch normalization (BN) and Darknet structures, which improve the accuracy and accelerate the convergence speed. The main advantages of one-stagemethods lie in their ability to detect small objects and their high efficiency. Because of these attributes, they are more suitable as the baseline for road damage detection.  
[PERSON] et al. used YOLO v4 to detect road damage on the RDD dataset [6] and introduced a generative adversarial network (GAN) for data augmentation. Additionally considering data augmentation are PG-GAN proposed by [PERSON] et al. [27] and the improved transfer learning proposed by [PERSON] et al. [3]. They all hope to use GAN to generate more diverse training samples to improve the generalization ability of networks in real road scenes. However, they did not consider semi-supervised training for the generated data, so they failed to give full play to the role of data augmentation. In addition, there are some algorithms of road damage detection assisted by 3D sensors and laser cameras. They utilize the depth information and sensing signals of the road surface to improve the location and classification of road damage [23; 28]. Relevant professional equipment is generally expensive, which makes it suitable for quantitative analysis of road damage prior to road maintenance, but it is not convenient for daily road damage detection.  
Almost all of the methods mentioned above are evaluated on the RDD dataset [3]. Based on this dataset, the BigData Road Damage Detection Challenge was held three times from 2018 to 2020. In a recent challenge, DiDi and the Shenzhen Urban Transport Planning Center achieved excellent performance by improving Faster RCNN and YOLO v4 [5]. This demonstrates that the road damage detection task has attracted much attention and has potential research value. Although its development is not mature at this stage, most methods rely on modification of object detection networks [7; 29; 30; 31]. With the release of the CNRDD dataset and its subsequent continuous updating, however, there will be more targeted research to promote the development of ADRD. Aiming at the regional dispersion of road damage, this paper proposes a road damage detection baseline with attention learning. It introduces attention fusion and attention normalization to damage detection for the first time and sheds light on ADRD with attention learning.