  Joint raveled or spalled damage (henceforth called joint damage) can affect
the safety and long-term performance of concrete pavements. It is important to
assess and quantify the joint damage over time to assist in building action
plans for maintenance, predicting maintenance costs, and maximize the concrete
pavement service life. A framework for the accurate, autonomous, and rapid
quantification of joint damage with a low-cost camera is proposed using a
computer vision technique with a deep learning (DL) algorithm. The DL model is
employed to train 263 images of sawcuts with joint damage. The trained DL model
is used for pixel-wise color-masking joint damage in a series of query 2D
images, which are used to reconstruct a 3D image using open-source structure
from motion algorithm. Another damage quantification algorithm using a color
threshold is applied to detect and compute the surface area of the damage in
the 3D reconstructed image. The effectiveness of the framework was validated
through inspecting joint damage at four transverse contraction joints in
Illinois, USA, including three acceptable joints and one unacceptable joint by
visual inspection. The results show the framework achieves 76% recall and 10%
error.
