  Due to the varying intensity of pavement cracks, the complexity of
topological structure, and the noise of texture background, image
classification for asphalt pavement cracking has proven to be a challenging
problem. Fatigue cracking, also known as alligator cracking, is one of the
common distresses of asphalt pavement. It is thus important to detect and
monitor the condition of alligator cracking on roadway pavements. Most research
in this area has typically focused on pixel-level detection of cracking using
limited datasets. A novel deep convolutional neural network that can achieve
two objectives is proposed. The first objective of the proposed neural network
is to classify presence of fatigue cracking based on pavement surface images.
The second objective is to classify the fatigue cracking severity level based
on the Distress Identification Manual (DIM) standard. In this paper, a databank
of 4484 high-resolution pavement surface images is established in which images
are taken locally in the Town of Blacksburg, Virginia, USA. In the data
pre-preparation, over 4000 images are labeled into 4 categories manually
according to DIM standards. A four-layer convolutional neural network model is
then built to achieve the goal of classification of images by pavement crack
severity category. The trained model reached the highest accuracy among all
existing methods. After only 30 epochs of training, the model achieved a crack
existence classification accuracy of 96.23% and a severity level classification
accuracy of 96.74%. After 20 epochs of training, the model achieved a pavement
marking presence classification accuracy of 97.64%.
