Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation
This work addresses a data scarcity problem for researchers and practitioners in infrastructure inspection, though it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the lack of large-scale public datasets for pavement damage segmentation by introducing Pavementscapes, a dataset of 4,000 high-resolution images with 8,680 manually labeled damage instances across six classes, and they established baselines using deep neural networks for this task.
Pavement damage segmentation has benefited enormously from deep learning. % and large-scale datasets. However, few current public datasets limit the potential exploration of deep learning in the application of pavement damage segmentation. To address this problem, this study has proposed Pavementscapes, a large-scale dataset to develop and evaluate methods for pavement damage segmentation. Pavementscapes is comprised of 4,000 images with a resolution of $1024 \times 2048$, which have been recorded in the real-world pavement inspection projects with 15 different pavements. A total of 8,680 damage instances are manually labeled with six damage classes at the pixel level. The statistical study gives a thorough investigation and analysis of the proposed dataset. The numeral experiments propose the top-performing deep neural networks capable of segmenting pavement damages, which provides the baselines of the open challenge for pavement inspection. The experiment results also indicate the existing problems for damage segmentation using deep learning, and this study provides potential solutions.