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Crack detection plays a key role in automated pavement inspection. Although alarge number of algorithms have been developed in recent years to further boostperformance, there are still remaining challenges in practice, due to thecomplexity of pavement images. To further accelerate the development andidentify the remaining challenges, this paper conducts a comparison study toevaluate the performance of the state of the art crack detection algorithmsquantitatively and objectively. A more comprehensive annotated pavement crackdataset (NHA12D) that contains images with different viewpoints and pavementstypes is proposed. In the comparison study, crack detection algorithms weretrained equally on the largest public crack dataset collected and evaluated onthe proposed dataset (NHA12D). Overall, the U-Net model with VGG-16 as backbonehas the best all-around performance, but models generally fail to distinguishcracks from concrete joints, leading to a high false-positive rate. It alsofound that detecting cracks from concrete pavement images still has huge roomfor improvement. Dataset for concrete pavement images is also missing in theliterature. Future directions in this area include filling the gap for concretepavement images and using domain adaptation techniques to enhance the detectionresults on unseen datasets.

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