Traffic sign recognition using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/14058 |
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10361-140582022-01-26T10:08:23Z Traffic sign recognition using deep learning Amir, Afia Binte Nisa, Umme Habiba Shafi, Ali Ashab Reza, Md. Rafid-Ur Uddin, Dr. Jia Department of Computer Science and Engineering, Brac University Traffic Sign Recognition Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-34). Traffic sign recognition plays a significant role in modern automated driver assisting systems and showing information about safety measures. It is a technology that allows users to recognize traffic signs in real-time, typically in videos, or sometimes just in photos. Poor identification of traffic signs cause road accidents. Moreover In adverse situation like heavy rain,foggy weather or sleepy driver can misidentify a traffic sign that may cause the death of hundreds of people. As a result identification of traffic signs properly has become an obligatory topic for research. In this research, we have used convolutional neural network for detecting and classifying the road signs accurately. We have proposed five Keras models of CNN and compared their results. The main challenge of this research is dealing with noise in images such as ads, parked vehicles, pedestrians, and other moving objects or background objects that made the recognition much more difficult. Not only the objects but also various environmental issues like the reflection of light, rainfall, fog etc has affected the research. In order to conduct this research we have collected our own data-set. We roamed around Dhaka city and clicked pictures of the traffic signs as there is no benchmark data-set available in the perspective of Bangladesh. For 500 images this model gives out an accuracy of 63%. There have been many researches in this field but our one is unique as it is tested on our own collected data-set on Bangladesh’s perspective. Recognizing traffic signs has become a part of our daily essentials as road safety depends on it, on a large scale which made it an obligatory topic for research. Afia Binte Amir Umme Habiba Nisa Ali Ashab Shafi Md. Rafid-Ur-Reza B. Computer Science 2020-10-14T04:03:26Z 2020-10-14T04:03:26Z 2019 2019-12 Thesis ID: 15301039 ID: 15301044 ID: 15301099 ID: 16101229 http://hdl.handle.net/10361/14058 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 34 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Traffic Sign Recognition Deep Learning |
| spellingShingle |
Traffic Sign Recognition Deep Learning Amir, Afia Binte Nisa, Umme Habiba Shafi, Ali Ashab Reza, Md. Rafid-Ur Traffic sign recognition using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Uddin, Dr. Jia |
| author_facet |
Uddin, Dr. Jia Amir, Afia Binte Nisa, Umme Habiba Shafi, Ali Ashab Reza, Md. Rafid-Ur |
| format |
Thesis |
| author |
Amir, Afia Binte Nisa, Umme Habiba Shafi, Ali Ashab Reza, Md. Rafid-Ur |
| author_sort |
Amir, Afia Binte |
| title |
Traffic sign recognition using deep learning |
| title_short |
Traffic sign recognition using deep learning |
| title_full |
Traffic sign recognition using deep learning |
| title_fullStr |
Traffic sign recognition using deep learning |
| title_full_unstemmed |
Traffic sign recognition using deep learning |
| title_sort |
traffic sign recognition using deep learning |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14058 |
| work_keys_str_mv |
AT amirafiabinte trafficsignrecognitionusingdeeplearning AT nisaummehabiba trafficsignrecognitionusingdeeplearning AT shafialiashab trafficsignrecognitionusingdeeplearning AT rezamdrafidur trafficsignrecognitionusingdeeplearning |
| _version_ |
1814307483627290624 |