This Project entitled “Traffic Sign Recognition System (TSRS)”, submitted by Nazmul Hasan and Tanvir Anzum to the Department of Computer Science and Engineering, Daffodil International University, has been accepted as satisfactory for the partial fulfillment of the requirements for the degree B .Sc. In localization part, where traffic sign region is located and identified by creating a rectangular area. After that, the rectangular box in recognition part gave the result for which traffic sign is located in that specific region.
The main reason for most road accidents is not knowing or not recognizing the traffic sign. The meaning of traffic sign is any object, device or sign on the road that the object must convey to road users, or any specified class of road user, restrictions, prohibitions, warnings or information of any description. Traffic Sign Detection and Recognition System (TSRS) is an important issue to reduce traffic and increase the stability of the self-driving car without any incident.
The traffic sign has been used in Bangladesh since the 1930s and they are inadequate in the current traffic situations. In this paper, we proposed this system to recognize traffic signs with supervised classification algorithms.
Rational of the Study
For this, we will do research in the field of Artificial Intelligence (AI) and Machine Learning (ML) and then we started looking for some ideas. Then we found so many classification methods to classify traffic signs from it using large amounts of images and video datasets. Finally, we have come to a great idea and it is called "Traffic Sign Recognition System (TSRS)" Based on image processing.
Research Questions
Expected Outcome
Project Management and Finance
Report Layout
Moreover, this chapter shows the procedural approaches of SVM, CNN and other Machine Learning classifier. And within the final area of this chapter, to approve the display as well as appear, the accuracy of the classifier is displayed. In our project how to have an impact on society and environment, discuss good or bad about this chapter.
The chapter concludes by presenting the limitations of our works that may be the long-term scope of others working in this field.
BACKGROUND
Terminologies
Related Works
In 2017, Shi et al. SVM to detect which region of the image contains a traffic sign [7]. Faster recurrent convolutional neural networks and Single Shot Multi Box detector with multiple feature extraction they used in 2019 to detect traffic signs. But in this time, they split a total of 900 images into 600 training and 300 as test data to detect traffic signs [9].
In 2017, Yassmina Saadna and Ali Behloul discussed an approach to detect and recognize road signs. Their main goal was to find detection methods for locating the areas of interest containing road signs. To classify the traffic signs, they used a neural network and four types of traffic signs were used: stop sign, prohibition sign, priority sign and speed limit sign.
So, traffic sign detection and recognition is necessary to build an autonomous car driving system. Independently proposed the Haar features and Histograms of Oriented Gradients (Hog) as strong features for the area of the vehicle.
Comparative Analysis and Summary
They show some of the preliminary vehicle next comes about using compressive estimates of the main infrared video. These incentives also contributed to an unused focus in the questions of high performance CNN frameworks. The plans of these frameworks have also observed that their execution is pushed forward by using increasingly significant and progressively broad structures [16].
Scope of the problem
Challenges
Research Subject and Instrumentation
Data Collection Procedure
Statistical Analysis
Proposed Methodology
- Convolutional Neural Network
- Support Vector Machin
One as an input image and another as a filter of the input image to produce an output image. In this layer, an activation function "relu" is applied to the feature maps to increase the non-linearity of the network. The weights and feature detectors are adjusted to help optimize model performance.
In the SVM model, we can plot each element as a point in n-dimensional space, where the value of each feature is the value of a particular coordinate (where n is a number of features). The second step is to check the shape fit over the gray images to find possible sign locations. This module exploits the regularity of traffic signs with their color and shape with high competence.
To describe the methodology, this paper varies the size of the cells and blocks to obtain different image sizes. Here we show all the steps of SVM to recognize the traffic signs for TSRS.
Implementation Requirements
- Hardware/Software Requirements
Experimental Setup
Experimental Result & Analysis
For the SVM method, we obtained an accuracy of 98.33% when we split the total data set in the ratio of 80:20 for training and testing purposes. The detection part uses image processing techniques that creates a contour on each frame and finds all ellipses or circles between those contours.
Discussion
Impact on Society
Impact on Environment
Ethical Aspects
Sustainability Plan
Summary of the Study
Conclusions
Implication for Further Study
CONCLUSION, RECOMMENDATIONS AND FUTURE WORK. Traffic issues our ambition is to implement a system with the calculation of the distance from the table to the car in traffic. Simplified neural networks with intelligent detection for road traffic sign recognition. eds) Advances in Information and Communication. 6] Sandy, A., Chih-Jung, C., Hsueh-Ming H.: Real-time traffic sign recognition using color segmentation and SVM.
Therefore, we are inclined to believe that our application "Traffic Sign Recognition System (TSRS)" will be extremely positive and effective for users. 34;A Review of Traffic Sign Detection and Classification Methods", International Journal of Multimedia Information Retrieval, 2017. 34;Traffic Sign Detection and Classification Using Color Feature and Neural Network International Conference on Intelligent Control Power and Instrumentation (ICICPI), 2016.
We also collect some random videos and crop the traffic sign area to build a real dataset. The SVM classifier for traffic sign detection and recognition yielded an accuracy of 98.33% (80:20 data split), while with the CNN method we achieved a training accuracy of 99.56% and a validation accuracy of 96.40%. This document works with the aim of recognizing road signs to help the driver determine which road sign is ahead.
This study paper is intended to present an original approach of effective traffic sign detection and recognition to TSRS design. In the future, we want to increase the number of traffic sign classes with a large amount of quality data. In order to provide a complete system to overcome traffic problems, our ambition is to implement a system by calculating the distance from the car to the traffic sign.
Sandy, A., Chih-Jung, C., Hsueh-Ming H.: Real-time traffic sign recognition using color segmentation and SVM. In: Conference on Systems, Signals and Image Processing (IWSSIP Jian-He, S., Huei-Yung, L.: A vision system for traffic sign detection and recognition.
