Pothole in roads constitutes a major problem
for both citizen and government. The pothole can create
serious damages to the vehicles such as vehicle flat tires,
scratches, dents and leaks. Thus, to detect these
potholes and provide maintenance is highly time
consuming and required lot of man power. Therefore
this paper purposes a pothole detection system which
is used for detecting the pothole and to analyze the
image to determine its dimension. For detecting the
pothole captured road images are inserted in the
system then feature extraction and classifier performs.
Lastly the predictor is done with the detection of
pothole based on machine learning. The pothole
detection system is derived from that assumptions that
any strong dark edges within the extracted surfaces
estimated a pothole if it observes certain constraints.
Such as size color. Any outlines that meet these
conditions are estimated as pothole by the algorithm.
On the other hand for analyzing the image of pothole
starts by converting the road surface images to gray
scale and calculate the SURF points using Manhattan
and Euclidean algorithm for calculating the dimensions
of the pothole in the MATLAB environment, further
comparing the system result obtain by these algorithms
with the result calculated manually in order to find the
error percentage of the system.
Keywords : Pothole Detection System, Manhattan Algorithm, Euclidean Algorithm, Knearest Neighbor Algorithm, MATLAB.