
Abstract Due to the serious threats to road safety caused by pavement distress, it has drawn much attention to seek efficient road crack detection methods for automatic pavement condition evaluation. In recent detectors, structure information of cracks has not been utilized effectively, which leads to the high sensitivity to crack-like noises. In this paper, we propose a novel crack detection method to recognize cracks out of noises. We verified our method in a real pavement crack dataset. Extensive experiments demonstrate the state-of-the-art results of the proposed method.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
