Abstract:
Pavement distress and wear detection is of prime importance in transportation engineering.
Due to degradation, potholes and different types of cracks are formed and they have to be
detected and repaired in due course. Estimating the amount of filler material that is needed to
fill a pothole is of great interest to prevent any shortage or excess, thereby wastage, of filler
material that usually has to be transported from a different location. Metrological and
visualization properties of a pothole play an important role in this regard. Using a low-cost
Kinect sensor, the pavement depth images are collected from concrete and asphalt roads.
Meshes are generated for better visualization of potholes. Area of pothole is analyzed with
respect to depth. The approximate volume of pothole is calculated using trapezoidal rule on
area-depth curves through pavement image analysis. In addition pothole area, length, and
width are estimated. Accuracy of Kinect Sensor in data acquisition is addressed while
imaging on water filled potholes to check its performance in rainy environment. Percentage
errors are provided to have a comparison between actual and calculated measurements.
