DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation
University of Massachusetts Lowell
Abstract
Cracks are important indicators of pavement structural and operational conditions. Early pavement crack detection and treatments can help extend pavement service life, reduce fuel consumption, and improve safety and ride quality. Pavement distress surveys have traditionally been performed manually by visually inspecting the roads, which is labor-intensive and time-consuming. Therefore, computer-vision-based automated crack detection has great practical significance in pavement maintenance and traffic safety. Traditional image processing techniques are sensitive to noise in images and are thus likely to miss detecting some cracks due to the crack texture variety, complex lighting conditions, and various similar…
Citation impact
- FWCI
- 25.27
- Percentile
- 100%
- References
- 58
Authors
5Topics & keywords
- Segmentation
- Computer science
- Feature (linguistics)
- Scale (ratio)
- Image segmentation
- Artificial intelligence