NHA12D: A New Pavement Crack Dataset and A Comparison Study of Crack Detection Algorithms

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.