NHA12D: A New Pavement Crack Dataset and A Comparison Study of Crack Detection Algorithms
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Abstract
Crack detection plays a key role in automated pavement inspection. Although a large number of algorithms have been developed in recent years to further boost perfor- mance, there are still remaining challenges in practice, due to the complexity of pavement images. To further acceler- ate the development and identify the remaining challenges, this paper conducts a comparison study to evaluate the per- formance of the state of the art crack detection algorithms quantitatively and objectively. A more comprehensive an- notated pavement crack dataset (NHA12D) that contains images with different viewpoints and pavements types is proposed. In the comparison study, crack detection al- gorithms were trained equally on the largest public crack dataset collected and evaluated on the proposed dataset (NHA12D). Overall, the U-Net model with VGG-16 as backbone has the best all-around performance, but models generally fail to distinguish cracks from concrete joints, leading to a high false-positive rate. It also found that detecting cracks from concrete pavement images still has huge room for improvement. Dataset for concrete pave- ment images is also missing in the literature. Future di- rections in this area include filling the gap for concrete pavement images and using domain adaptation techniques to enhance the detection results on unseen datasets.
Description
Keywords
Journal Title
Conference Name
Journal ISSN
2684-1150
Volume Title
Publisher
Publisher DOI
Rights
Sponsorship
EPSRC (EP/V056441/1)