Abstract—This paper presents a computer vision system whose
aim is to detect and classify cracks on road surfaces. Most of the
previous works consisted of complex and expensive acquisition
systems, whereas we have developed a simpler one composed
by a single camera mounted on a light truck and no additional
illumination. The system also includes tracking devices in order to
geolocalize the captured images. The computer vision algorithm
has three steps: hard shoulder detection, cell candidate proposal,
and crack classification. First the region of interest (ROI) is delimited
using the Hough transform (HT) to detect the hard shoulders.
The cell candidate step is divided into two substeps: Hough
transform features (HTF) and local binary pattern (LBP). Both
of them split up the image into nonoverlapping small grid cells
and also extract edge orientation and texture features, respectively.
At the fusion stage, the detection is completed by mixing those
techniques and obtaining the crack seeds. Afterward, their shape
is improved using a new developed morphology operator. Finally,
one classification based on the orientation of the detected lines has
been applied following the Chain code. Massive experiments were
performed on several stretches on a Spanish road showing very
good performance.