  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
performance, there are still remaining challenges in practice, due to the
complexity of pavement images. To further accelerate the development and
identify the remaining challenges, this paper conducts a comparison study to
evaluate the performance of the state of the art crack detection algorithms
quantitatively and objectively. A more comprehensive annotated pavement crack
dataset (NHA12D) that contains images with different viewpoints and pavements
types is proposed. In the comparison study, crack detection algorithms 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 pavement images is also missing in the
literature. Future directions in this area include filling the gap for concrete
pavement images and using domain adaptation techniques to enhance the detection
results on unseen datasets.
