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               India is one of the most populous
country, roads
are the main mode of transportation in this developing country. But due to the
heavy use of roads, there is a high
amount of wear and tear carried out. Since these roads cannot sustain itself
for a long time, a timely maintenance is expected to be carried out in order to
prevent the formation of potholes. The manner in which a pothole is formed
depends on the type of bituminous pavement surfacing. The heavy traffic on the
road is the primary reasons for the
fatiguing of the road surface, resulting in the formation of the crack. These
depressions collect water and allow the water to mix with the asphalt. When
vehicles drive through such holes the water is expelled along with some of the
asphalt, and this slowly creates a cavity underneath the crack. If a regular
road maintenance is neglected, the road surface will eventually collapse into
the cavity, resulting in a visibly huge pothole over the surface. In order to
repair these roads in a timely manner, it is necessary that the entity knows
which area is affected by the pothole or decaying road section is located and
an automated process could assist with this.

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               All these reasons demand that it is
important to collect information of the road conditions and through a series of
processing and analyzing the obtained
information, appropriate conclusions are derived which in turn, warn the
officials of the respective area. The simplest and highest accuracy approach to
this is to click and upload pictures to the interface provided by the official,
but this involves a strong participation from the users as well as manual image
analysis. Thus, an automated detection sounds more promising in this case.

              Our approach
includes the use of a computer vision based system. In this system, a two-dimensional image of roads is
used. The digital images are captured by the camera and are processed to
capture the information related to road anomalies. In a 2D image-based approach,
the system extracts the texture measure based on the histogram as the features of the image region, and the nonlinear
support vector machine was built up to identify a potential region is a pothole
or not.




               Taehyeong Kim and Seung-Ki 1
Ryu proposed a detection system which starts with noise removal, followed by
adjustment of brightness and simplification of video by binarization. Then,
noise removal is applied to the binarized image. After noise removal, the process of extraction of the outlines of the
segmented objects is carried out. Extraction is followed by selection and
square zoning for the objects. After all these processes, desired pothole area
information is returned.

              Sudarshan Rode 2
proposed a pothole detection system which is divided into three subsystems.
First is sensing subsystem which senses the potholes encountered by it, by
using accelerometer or by camera which scans the road. Both are mounted on the
car. Then communication subsystem which transfers the information between Wi-Fi
access point and mobile node. Access Point broadcasts the data about potholes
in its area.

              Eriksson et al. 3 studied
mobile sensing of roads to monitor and report any potholes. The system used accelerometer
and GPS for detection and location respectively. Cars give detections which are
fed to a central server.










               The proposed system utilizes a Raspberry Pi, as the main processor for performing the
image processing and detecting potholes. A Raspberry Pi is a development board embedded with ARM
processor and capable running UNIX based operating system. The model used has an onboard 1GB of RAM,
which will make it capable of performing the image processing along with the
detection. It also has an interface which
supports the raspberry pi to connect to a camera module.

              An additional 3G USB modem is utilized as the network interface so
that Raspberry Pi is get connected to the internet. This modem is attached to
Raspberry Pi, therefore it can make the Raspberry Pi is able to transmit any
defects or potholes presence on road in real-time. It also has a micro SD card slot which will be
used to temporarily store images 1.


              An open-source library of image processing called
OpenCV is utilized as the framework for the image processing development.
OpenCV is a library which is designed for a computational efficiency for image
processing and manipulation. OpenCV supports Linux operating system which is
suitable to be developed inside the Raspberry
Pi. It also has interfaces to python, C,
C++, and Java 1.


              Video has been captured using a camera module interfaced with
raspberry pi. Frames of the video are extracted and the individual frame is
considered as an image which is further processed.

flowchart of image processing.






              The first step after frame extraction was the conversion of the RGB
image into grayscale using standard techniques to make processing of image faster 2. An example grayscale image is shown in Figure 3.

Example of grayscaling.


              After grayscaling we perform three different blurs on the image.
The image is firstly blurred using averaging then with gaussian filter and lastly with median blur so to remove unwanted
noise from the image.

Image after blurring.


               To achieve more accurate edge detection from a depth image we have
modified the process using morphological operations. These operations are
generally a collection of nonlinear operations carried out comparatively on the
ordering of pixels without affecting their numerical values. The key operators
for morphological operations are erosion and dilation. We have used erosion
after blurring operations which is
followed by two iterations of dilation 3.

     The pothole detection is utilizing
canny edge detection technique. The detection technique is a multi-stage method
to detect a wide range of edges in images. Canny edge detection goes through
five stages as follows:

Gaussian filter to smooth the image in order to remove the noise
Find the
intensity gradients of the image
non-maximum suppression to get rid of spurious response to edge detection
double threshold to determine potential edges
edge by hysteresis: Finalize the detection of edges by suppressing all the
other edges that are weak and not connected to strong edges 3.

Example of canny edge detection.

              We use
Otsu’s method for reduction of a gray level image to a binary image. The
algorithm assumes that the image contains two classes of pixels following
bi-modal histogram (foreground pixels and background pixels), it then
calculates the optimum threshold separating the two classes so that their
combined spread (intra-class variance) is minimal, or equivalently (because the
sum of pairwise squared distances is constant), so that their inter-class
variance is maximal 4.


            The system then uses contour detection technique. For better
accuracy, use binary images. So we have applied threshold
and canny edge detection. Contours can be explained simply as a curve joining
all the continuous points (along the
boundary), having same color or
intensity. The contours are a useful tool for shape analysis and object
detection and recognition. Thus, it is very useful in pothole detection system.

            In order to detect circles in images, we make use of the
cv2.HoughCircles function
by making adjustments in the signature below:

cv2.HoughCircles(image, method, dp, minDist)

image: It is the image obtained after contour detection.

method: Defines the method to
detect circles in images.
We are using cv2.HOUGH_GRADIENT in our system.

dp: This parameter is the inverse ratio of the accumulator resolution
to the image resolution.

minDist: Minimum distance
between the center (x, y) coordinates of detected circles.

param1: Gradient value used to
handle edge detection in the Yuen et al. method.

param2: Accumulator threshold
value for the cv2.HOUGH_GRADIENT method.

minRadius: Minimum size of the
radius (in pixels).

maxRadius: Maximum size of the
radius (in pixels).


If circles are detected after all these steps, an email will be sent to
respective officials using SMTP


proposed system

A sample
block diagram of the proposed system is represented in the figure as shown


FIG.6. Generalized
diagram of the proposed system.

             In this system, a database of road images
and videos are collected. Videos are acquired from a roadside pole with a
camera module interfaced with raspberry pi. As the system is stationery, the
hassle of adjusting speed etc is eliminated. Hence, the images give a clear
view of the road from the roadside pole. These images go through image
processing steps for detection. If potholes are detected, the images and the
locations are sent to the official with the use of emails.













              Potholes have nothing but
negative effects and hence it must be eradicated as soon as possible. The
current system includes the use of manual detection by people who are willing
to contribute for the betterment of the road. Thus, it is important that manual
labor approach is kept to a minimum and switched to an automatic approach

The system will be installed in a fixed position on the light poles
which ensures less handling.  Also, this
system keeps a track of the negligence and delay. The system makes use of
Raspberry Pi, which has a low cost and high compatibility with other interfaces, we also make use of 2D vision-based approach, this makes our system
more affordable.

              The system also detects potholes
in time without damaging the cars for potholes detection. Thus, making the
system more feasible and favorable.

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