Promoting Automatic Detection of Road Damage: A High-Resolution Dataset, a New Approach, and a New Evaluation Criterion

Using deep learning to detect road damage can significantly improve the effectiveness of road maintenance. To promote the development of road damage detection, we construct a high-resolution road damage data set named Asphalt Road Surface Disease Dataset(ARSDD), comprising 2297 images used in a real-world project. The annotation process is under the guidance of the road maintenance department, and it has more accurate labels and appropriate damage types. Most current road damage detection models are anchor-based, where one anchor corresponds to one sample. Therefore, these models are primarily limited by the setting of pre-defined anchors. Road damages have more extreme aspect ratios and scales than natural objects, and the general settings of anchors are inappropriate for road damage. In this paper, we propose a road damage detection model based on an improved adaptive training sample selection strategy, which can reduce manual anchor settings and is suitable for road damage detection. Moreover, as slight road damages tend to lose information during down-sampling, a cross-layer attention feature pyramid network is designed to compensate for this degradation in the spatial dimensions. While testing the ARSDD dataset, we find that the evaluation criterion for general object detection is unsuitable for road damage detection and propose a new post-processing method and diagonal-based evaluation criterion according to the characteristics of road damage. We validate our model using the 2018 Road Damage Dataset and our proposed dataset, and the results demonstrate the superiority of our model in road damage detection. Note to Practitioners —This paper was motivated by the problem of road damage detection, which is the key of intelligent road maintenance system. We comprehensively analyze the shortcomings of current publicly available content about road damage detection, including detection datasets, algorithms and evaluation metrics. Correspondingly, we first construct a road damage dataset from the real-world project, which contains six common categories of disease and is annotated under the guidance of the road maintenance department. Then we propose a detection model based on computer vision technology to improve the accuracy of damage detection. Finally, we propose a new post-processing method and a diagonal-based evaluation criterion based on the detection boxes. Any practitioner working on pavement inspection systems can use our released dataset and method to build a better-performing automated inspection system. In future research, we will try more refined inspection schemes and evaluation indicators, such as meshing the road surface images and evaluating the quality of the road based on the inspection results of each mesh.