Route Intensity Tracker using Machine Learning and Database Management

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 465
Route Intensity Tracker using Machine Learning and Database
Management
Ambati Maneesha1, Munukoti Likhit2
1 Dept. of Information Technology Army Institute of Technology, Maharashtra, India
2 Dept. of Computer Engineering Army Institute of Technology, Maharashtra, India
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Abstract - Due to the lack of maintenance on aging roads
and roads in maintenance and the insufficient use of proper
measures to prevent accidents, there has been a significant
rise in the number of road accidents. Furthermore, it is
advantageous to consider an alternative route after receiving
advance information about the road conditions. This study
delves into the utilization of image processing, object
recognition using machine learning for analyzing photos and
proactively alerting automobile drivers to potential hazards.
To achieve this, we will employ various techniques, including
image processing, image data cleaning methods, speed
breaker detection methods, pothole detection algorithms,
database management system. Additionally, we will focus on
enhancing and enhancing the accuracy and efficiency of this
application.
Key Words: Potholes, Speed bumps, Road intensity,
Machine Learning, Database management, Real time
detection, Convolutional Neural Network.
1.INTRODUCTION
India, the world's most populous country, boasts a rapidly
growing economy. In the context of Indian road conditions,
the development of an automated driver guidance system
holds paramount significance. The detection of speed-
breakers, potholes or roads in maintenance is crucial as
numerous accidents occur due to the abrupt appearance of
these road hazards.
Without a doubt, the inadequate maintenance of roads and
the sudden appearance of potholes are significant factors
contributing to road accidents. They not only jeopardize
safety but can also inflict substantial damage to a vehicle's
wheels and structure, along with a potential of accidents
happening and lives matters on it. This technology offers
early warnings to drivers as they select a roadway to their
destination about speed limiters or sudden changes in road
conditions, prompting immediate alerts.
Speed bumps are strategically positioned in areas where it's
essential to control vehicle speed for accident prevention,
such as accident-prone zones, sharp curves, congested
residential streets, and unguarded level crossings. The
physical characteristicsoftheseroadfeatures,includingtheir
height, length, and the presence of ramps, can exhibit
considerable variation. There are generally two types of
speed bumps: those thatareclearlymarkedwithvisiblepaint
or markings and those that have no such markings.Detecting
the former is a relatively simple task using image processing
techniques, but identifying unmarked speed bumps can be
exceptionally difficult, especially when they lack the
distinctive yellow or whitewarning stripes.Whilethehuman
visual system can recognize marked speed bumps from a
distance, it faces significant challenges when it comes to
identifying unmarkedones.Hence,weintroduceanapproach
for the detection of unmarked speed bumps, a critical step in
preventing accidents and ensuring a smoother driving
experience.
Potholes, being a form of road damage, act as important
indicators of structural problems within asphalt roads. The
precise detection of potholes is crucial for deciding on the
right maintenance and rehabilitation approaches forasphalt
pavements. Nonetheless, manual detection and assessment
methods are both costly and time-intensive. As a result,
numerous initiatives have been launched to create
technology capable of automatically identifying and
categorizing potholes, with the goal of improving survey
efficiency andadvancing pavement improvement initiatives.
1.1 Potholes detection
Detecting and distinguishing between potholes and smooth,
well-maintained roads is a crucial task with far-reaching
implications for road safety and upkeep. Machine learning
has emerged as a formidable tool in tackling this challenge,
offering multiple avenues to accurately identify and
differentiate these road conditions.
Convolutional Neural Networks (CNNs) have proven highly
effective in this context. By trainingtheseneuralnetworkson
a dataset encompassing images of both pothole-laden and
smooth roads, they can learnto identify distinguishingvisual
features. CNNs excel at recognizing patterns, textures, and
shapes, making them adept at identifying potholesinimages.
Table -1: Pothole detection Algorithms performances
Algorithm Estimated
Accuracy
Computer Vision (CNN) Approximately
85-95%

LiDAR-based Approximately
90-98%
Acoustic Sensors Approximately
80-90%
Machine Learning (Overall) Approximately
88-96%
However, among thesediverseapproaches,machinelearning
emerges as the superior choice for several compelling
reasons. Firstly, machine learning models possess the
capacity to adapt and refine their performance over time.
Secondly, machinelearningmodelscangrapplewithcomplex
and non-linear data relationships. Machine learning
algorithms, especially deep learning models like CNNs, are
adept at capturing these subtle differences and making
generalizations based on them.
A strong image recognition system may be easily built and
deployed by anyone thanks to the object detection API of
Tensorflow. It offersa varietyof pre-trained models (trained
on various datasets) that, after being tweaked, can beusedto
create unique classifiers, detectors, and recognizers. We've
chosen the "F-RCNN inception v2" model.
Transfer Learning applies the knowledge acquired while
resolving one problem to another, unrelated one. We may
make significant time savings with this method. In the
technique below, we choose any pre-trained model and then
refine it. To improve the pre-trained CNN, we get the new
dataset. The sameweights can be used to extract thefeatures
from the new dataset if it shares characteristics with the old
dataset. The dataset in our instance differs significantly from
the original dataset. In contrast to the later layers of CNN,
which become increasingly more focused on the specifics of
the classes in the original dataset, the earlier levels of CNN
contain more general properties.
The improved version of the inception network series is
called inception v2. The architectureof CNNs is intricate.The
first installment in this series is called Inception v1.
Convolution is performed on the input using Inception v1
using 3 distinct filter sizes (1x1, 3x3,and 5x5).Maxpoolingis
additionally utilized. The following conception module
receives the concatenated Outputs after being provided to it.
The number of input channels is limited by adding an
additional 1x1 convolution before the 3x3 and 5x5
convolutions because deep learning networks are
computationally expensive. The GoogLeNet neural network
architecture, which has nine of these inception modules,was
created using this inception module. Additionally, the
Inception v2 architecture is introduced to speed up
calculation.
1.2 Speed-bump detection
Detecting and telling the difference between marked and
unmarked speed bumps on the road is important for road
safety. Machinelearning can helpwiththistask,andthereare
different ways to do it.
One way is to use cameras and computer vision. This means
using cameras to look at the road and then using computer
programs to understand what the camera sees.
ConvolutionalNeuralNetworks(CNNs)areespeciallygoodat
this. We can teach these neural networks to recognize speed
bumps, especially those with lines or symbols painted on
them. CNNs are good at recognizing shapes, patterns, and
textures, so they can spot marked speed bumps.
Table -2: Speed bump detection Algorithm
performances
Algorithm Estimated
Accuracy
Computer Vision (CNN) Approximately 85-
95%
LiDAR-based Approximately 90-
97%
Acoustic Sensors Approximately 80-
90%
Machine Learning (Overall) Approximately 88-
96%
But why is machine learning the best choice? Here are some
reasons:
a. Combining Different Data: Machinelearningcanuse
information from cameras, LiDAR, and acoustic
sensors allat once. This makes it better at telling the
difference between marked and unmarked speed
bumps.
b. Handling Complexity: Roads and speed bump
markings can be different in many ways. Machine
learning is good at handling these differences and
adapting to new situations.
c. Improvement Over Time: Machine learning models
can get better as they see more data and as road
conditions change. This is important because roads
can change due to weather or repairs.
d. Real-TimeProcessing:Machinelearningcanworkin
real-time, so it can give drivers warnings or adjust a
vehicle's suspension right away when it detects a
speed bump. This helps with safety and comfort.

e. Scalability: Machine learning can be used on many
vehicles and roads without costing too much. It's a
cost-effective way to monitor roads.
Before we can use the CNN for our classification application,
it must be trained. During thetrainingphase,wegivetheCNN
a variety of images along with the expectedresultsorclasses.
In order to create an optimum network, this process is
repeated a certain number of times. To shorten the overall
training period, we applied the transfer learning strategy. In
transfer learning, the final classification layer of the
pretrained convolution neural network is changed. These
networks divide the data into 1000 or more classes after
being trained on millions of photos over several weeks. We
use the networks as-is and construct our network on top of
them because they are so well-optimized.
Fig-3: Schematic representation of CNN
Another way is to use LiDAR, which is like a laser scanner.
LiDAR sends out laser beams to make 3D maps of the road.
Machine learning can analyze these maps to find places
where the road is uneven, like where there are speed bumps.
LiDAR may not see markings, but it can still tell us where the
speed bumps are.
Acoustic sensors are another option. These sensors listen to
the sounds that cars make when they drive over different
road surfaces. Machine learning can learn the sounds of cars
going over speed bumps andusethattodetectthem.Acoustic
sensors can help, especially when it's hard to see speed
bumps.
1.3 Database Management System
The proposed database system has an important job: it helps
collect andstore essential informationaboutroads.Itfocuses
on where a journey starts, where it ends, and three points in
between. Plus, it also keeps track of things like speed bumps
and potholes. This system is a big step forward in how we
manage and keep our roads safe.
The system is really good at keeping track of where you're
going. It stores the starting point and endingpointofatrip,as
well as three places youmightstopalongtheway.Thismakes
it easier to plan routes for things like transportationservices,
emergency responses, and city planning. With this database,
you can quickly find out the order of these placesandhowfar
apart they are, which helps with making smart decisions
about travel routes.
But it's not justabout where you're going; it's alsoabouthow
safethe journey is. That's where the data about speedbumps
and potholes comes in. These road issues can be dangerous
fordrivers, passengers,andvehicles.So,it'sessentialtoknow
where they are and how bad they are. The database keeps
this information, and it can be used by people in charge of
roads and those who make navigation apps to take action to
keep roads safe and in good shape.
Fig-4: Front end parameters for input
The database also lets you look up information about speed
bumps and potholes. This means you can find out how often
they appear on certain routes. This is super helpful for city
planners, road maintenance teams,and safety organizations.
It helps them figure out which roads need fixing and where
they should focus their efforts. This way, they can use their
resources wisely and make the roads safer for everyone.
Additionally, the database can help with studying trendsand
patterns. By collecting data over time and in different areas,
we can learn more about where and when speed bumps and
potholes tend to show up. This information can guide
decisions on how to improve roads and reduce hazards.
Ultimately, this database system is like a smart assistant for
road information. It keeps track of where you're going, notes
any road issues like speed bumps and potholes, and lets you
find out how often these problems occur. It helps city
planners, road maintenance teams, and safety groups make
betterdecisionsaboutroadimprovements,ultimatelymaking
our roads safer and more efficient.
2. Methodology
Popular for finding objects in photos is the object detection
method YOLO. In order to forecast the vector ofthebounding
boxes and potholes, this technique uses a single neural
network. It operates by dividing images intoa5x5-sizedgrid.
Each grid cell can forecast the number of potential bounding
boxes and the degree of confidence (i.e., confidence score)
that the cell will contain the object, in this case a pothole. We
will get 5x5xN boxes as a result. The algorithm continues to
eliminate the boxes that are below a certain minimum
probability level because the majority of these boxes will
have a very low likelihood.

Fig-5: YOLO Algorithm
To eliminate all of the duplicate objects, the remaining
bounding boxes are then shifted towards a non-max
suppression. The YOLO V3 model usedinthisstudyistrained
using entire photos and the probability of the class within
bounding boxes. In comparisontotheoriginalapproachesfor
object detection, this method has many advantages. The
YOLO V3 model moves quickly. Because YOLO V3 works on
object detection as a regression problem, a complicated
pipeline is not required. This suggests that real-time video
processing is also possible with latency as low as 25ms.
Before detecting and making predictions, YOLO V3 examines
the image as a whole
Fig-6: Algorithm performance
Machine learning has emerged as the top most effective
methodology fordetecting potholes and speed bumps due to
its adaptability, precision, and versatility.Potholesandspeed
bumps present significant challenges in terms of road safety
and infrastructuremaintenance, and machinelearningoffers
a multitude of advantages in addressing these issues.
Models such as Convolutional Neural Networks (CNNs) and
other deep learning algorithms are especially adept at
identifying patterns and irregularities within visual data.
When these models are trained using a dataset that includes
images of roads featuring potholes and speed bumps, they
can effectivelylearntoidentifytheuniquecharacteristicsand
shapes associated with these road conditions. This level of
precision is challengingtoachieveusingtraditionalcomputer
vision methods.
Machine learningalsoexcelsinreal-timeprocessing,acritical
capability when it comes to pothole and speed bump
detection. These road hazards can emerge suddenly, posing
immediate risks to drivers. Machine learning models are
capable of analyzing data and making decisions in real-time,
enabling swift response mechanisms like warning signals,
adaptive suspension systems, or instant alerts to road
maintenance teams. Additionally, machine learning is cost-
effective and scalable. Once developed and trained, machine
learning models can be deployed across a broad spectrum of
vehicles and road infrastructure without incurring
substantialadditional costs.This scalability makes it feasible
to establish comprehensive road monitoring systems,
providinga cost-efficientsolutionsuitableforbothurbanand
rural areas.
Fig -7: Flow chart of the system
While alternative methods such as traditional computer
vision or manual inspections have been used for pothole and
speed bump detection, they often fall short in terms of
accuracy, efficiency, and scalability. Traditional computer
vision techniques may struggle to cope with variable road
conditions and lighting, whereas manual inspections are
labor-intensive and susceptible to human error.

3. CONCLUSION
The main reasons for accidents are going too fast and hitting
potholes. To prevent accidents, it's really important that the
car can automatically see and understand speed limit signs
and potholes. One of the hardest parts of making cars
smarter about this is recognizing speed bumps andpotholes
that don't have any markings. Also, it's important to check if
the methods we use to find speed bumps and potholes are
working well. Researchers have come up with ways to
measure how good the computer programs are at finding
them. But there's still a lot of work to do to make these
programs even better, especially when they have to look at
tricky or messy pictures and find potholes and speed bumps
really quickly. Overall, making these detection methods
better will help us use computerstounderstand picturesand
videos in all sorts of ways, like recognizing objects,
separating parts of pictures, and following things as they
move. This can be useful in fields like medicine,security,and
self-driving cars.
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