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Traffic Sign Board Recognition and Voice Alert

The document presents a Traffic Sign Board Recognition and Voice Alert System utilizing Convolutional Neural Networks (CNN) to enhance road safety by recognizing traffic signs and alerting drivers with voice notifications. Trained on the German Traffic Sign Benchmarks Dataset, the system achieves an accuracy of 98.52% and aims to provide real-time information to drivers, helping them make informed decisions. The proposed system addresses limitations of existing methods by offering improved detection, recognition, and alert mechanisms for traffic signs.

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2 views

Traffic Sign Board Recognition and Voice Alert

The document presents a Traffic Sign Board Recognition and Voice Alert System utilizing Convolutional Neural Networks (CNN) to enhance road safety by recognizing traffic signs and alerting drivers with voice notifications. Trained on the German Traffic Sign Benchmarks Dataset, the system achieves an accuracy of 98.52% and aims to provide real-time information to drivers, helping them make informed decisions. The proposed system addresses limitations of existing methods by offering improved detection, recognition, and alert mechanisms for traffic signs.

Uploaded by

daniyabasra0
Copyright
© © All Rights Reserved
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Traffic Sign Board Recognition and Voice Alert System

using Convolutional Neural Network


ABSTRACT:

To ensure a smooth and secure flow of traffic, road signs are essential. A major
cause of road accidents is negligence in viewing the Traffic signboards and
interpreting them incorrectly. The proposed system helps in recognizing the Traffic
sign and sending a voice alert through the speaker to the driver so that he/ she may
take necessary decisions. The proposed system is trained using Convolutional
Neural Network (CNN) which helps in traffic sign image recognition and
classification. A set of classes are defined and trained on a particular dataset to
make it more accurate. The German Traffic Sign Benchmarks Dataset was used,
which contains approximately 43 categories and 51,900 images of traffic signs.
The accuracy of the execution is about 98.52 percent. Following the detection of
the sign by the system, a voice alert is sent through the speaker which notifies the
driver. The proposed system also contains a section where the vehicle driver is
alerted about the traffic signs in the near proximity which helps them to be aware
of what rules to follow on the route. The aim of this system is to ensure the safety
of the vehicle’s driver, passengers, and pedestrians.

EXISTING SYSTEM:

 Yadav et. al. employed the Support Vector Machine technique. The dataset
was divided into 90/10 for training and testing purposes, and it employs
linear classification. To achieve the desired result, a series of phases called
Color Segmentation, Shape Classification, and Recognition were followed.
 Anushree.A et. al. used Raspberry Pi in detecting and recognizing Traffic
Signs with much less coding. However, it requires the Raspberry Pi board at
one's discourse for implementation which is quite costly.
 S. Harini et.al. introduced Another way of Traffic sign recognition is picture
intensive. A video is acquired and broken down into frames. Image
preprocessing is done which includes separating the foreground and the
background, thinning and contrast enhancement. The signs are then
categorised as hexagonal, triangular, or circular in shape and transmitted for
template matching after these operations. The objects with some definite
shape are matched from the pre-trained algorithm.

DISADVANTAGES OF EXISTING SYSTEM:

 While much research exit on both the automatic detection and recognition of
symbol-based traffic sign, and the recognition of text in real scenes there are
far less research focused specifically on the recognition of text on traffic
information signs.
 One of the main limitations preventing the deep learning from being applied
to a large set of traffic-sign categories is a lack of an extensive dataset with
several hundred different categories and a sufficient number of instances for
each category.
 Several of the existing systems, focused only on the traffic-sign recognition
(TSR) and ignored the much more complex problem of the traffic-sign
detection (TSD) where finding an accurate location of a traffic sign is
needed.
 Many other traffic-sign classes that are not included in the existing
benchmarks can be much more difficult to detect as they have a high-degree
of variation in appearance.
PROPOSED SYSTEM:

 In our proposed system, we develop the Traffic Sign Board Recognition and
Voice Alert System using Convolutional Neural Network. Our system will
able to detect, recognize and infer the road traffic signs would be a
prodigious help to the driver.
 The objective of an automatic road signs recognition system is to detect and
classify one or more road signs from within live color images.
 In this base paper we provide alertness to the driver about the sign using
voice of the detected sign board. The system provides the driver with real
time information from road signs, which consist the most important and
challenging tasks. Next generate an acoustic warning to the driver in
advance of any danger. This warning then allows the driver to take
appropriate corrective decisions in order to mitigate or completely avoid the
event.

ADVANTAGES OF PROPOSED SYSTEM:

 The accuracy of the proposed system is 97% and this model turned out to
give the best accuracy as compared to the other models that we analysed in
the existing.
 If a certain image is not containing a traffic sign, then the user gets a prompt
of “No Sign Detected” is also implemented in our model.
 Despite some missed detections, the detector still preforms extremely well
even for several difficult cases.
 A good performance is also presented, where all traffic-sign detections are
displayed for a couple of full-resolution images.
 The system provides an efficient deep network for learning a large number
of categories with efficient and fast detection.

SYSTEM ARCHITECTURE:

Pre-
processing CNN Model Predict the 44 Performance
Traffic Sign and Feature Architecture traffic Analysis and
dataset Selection classification Graph
with voice alert

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

 System : Pentium i3 Processor.


 Hard Disk : 500 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 4 GB

SOFTWARE REQUIREMENTS:
 Operating system : Windows 10.
 Coding Language : Python
 Web Framework : Flask

REFERENCE:

Krish Sukhani Radha Shankarmani Jay Shah Krushna Shah, “Traffic Sign Board
Recognition and Voice Alert System using Convolutional Neural Network”, IEEE
Conference, 2021.

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