# A New Road Damage Detection Baseline with Attention Learning
## 6 Conclusions and Discussion  
Road damage detection is very important for road maintenance. At this stage, all practical road damage detection solutions depend on expensive equipment with multiple sensors. However, many underdeveloped areas cannot find pavement problems in time because they cannot afford high-priced equipment. This leads to missing the best repair time for road damage. With the success of deep learning in multiple vision tasks, road damage detection based on deep learning brings hope of moving away from the dependence on expensive equipment. In order to introduce deep learning to ADRD, this paper first presents a road damage detection dataset from China, called the CNRDD. It contains 4319 images with 21,552 professional annotations. The image size is 1600 \(\times\) 1200 pixels. According to evaluation standard for highway technical conditions (JTG5210-2018), road damage is divided into eight categories. In addition to strengthening, each category also contains three degree labels. Compared with existing datasets, the CNRDD dataset is more challenging and practical. In order to further promote the development of ADRD, this paper also proposes a road detection framework based on attention learning. It utilizes edge detection and attention fusion to provide the salient region for feature learning, and then introduces NAM to suppress the weights of non-salient features. Thus, the influence of sparse damage-pixel distribution and similar appearance between types of damage on detection is effectively alleviated. After two-stage training, the performance of the proposed method is further improved on the RDD2020 and CNRDD datasets. The proposed method sheds light on road damage detection based on attention learning. Nevertheless, according to the experimental analysis described in Section 5, road damage detection based on deep learning remains challenging. It still needs more researchers to work together to solve. Therefore, this paper discusses future expandable work from the following three aspects. Figure 8: Precision–Recall curves from different methods. The data of (**a–c**) are from YOLO v5 [15], Faster R-CNN [12] and the proposed method, respectively. Different colors represent the detection results of different types of damage, and the corresponding relationship is shown in the legend. Standardized datasets. The quantity of existing road damage detection data greatly limits the development of ADRD, which is also the main reason for publishing the CNRDD dataset in this paper. However, a CNRDD dataset is not enough to change the research status. Researchers may make further efforts to achieve breakthroughs in the quantity of data with annotations. Then, standardization of road datasets is gradually formed from the perspective of annotation specification and classification basis. This not only helps to improve the performance of road damage detection algorithms, but also provides support for unified access to future intelligent road maintenance systems. 2. Optimized similarity measurement. A large number of approximate appearances is another main reason why road damage is difficult to be detected correctly. To solve this problem, researchers should not only improve the discrimination of salient features through attention learning, but also optimize the similarity measurement of feature vectors. Aspects such as feature alignment and fine-grained classification strengthen the recognition and location of road damage so as to alleviate false detection and missing detection. 3. Conditional transfer learning. Although transfer learning is an effective strategy to improve the generalization ability of algorithms, it needs to be based on the premise that the algorithm is excellent in a single data domain. Like the current road damage detection algorithm based on deep learning, the learned features cannot meet the actual needs in the specified domain. On this basis, a cross-domain bridge established by transfer learning is meaningless to the extraction of road damage features.