An efficient traffic management system to detect lane rule violation using Real-time Object Detection
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| פורמט: | Thesis |
| שפה: | English |
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Brac University
2021
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| גישה מקוונת: | http://hdl.handle.net/10361/14986 |
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10361-149862022-01-26T10:21:51Z An efficient traffic management system to detect lane rule violation using Real-time Object Detection Arnob, Faed Ahmed Fuad, Md. Azmol Nizam, Abu Tahir Siam, Arifin Tanjim Islam, Md. Motaharul Noor, Jannatun Department of Computer Science and Engineering, Brac University Automatic License Plate Recognition (ALPR) Hough Line Transform YOLO Object Detection Fog Computing Optical Character Recognition (OCR) Computer Vision Data Traffic Management System (Computer system) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-57). There has been an upsurge in the number of issues with Bangladesh’s present traffic control system. Hence, several accidents have occurred frequently. The two primary causes of a rise in the number of injuries are violations of traffic laws, such as illegal lane changes and excessive speeding. Here we have presented extensive research with an intention to resolve the current traffic management system using real-time object detection. In our proposed system, an edge node will detect the lane-based rule violation and send the data to the nearest intermediary node. Afterward, License plates as objects will be detected using YOLO object detection executed in the intermediary computing device. Finally, extracted license plate images from the intermediary nodes will be sent to BRTA traffic servers to detect the violator’s Bangla license plate number using pytesseract. We have built a data set of 1450 images for object detection and achieved an accuracy of 91%. Our system will assist the traffic control department in identifying those responsible for traffic rule violations and ensuring that the laws are strictly enforced. Faed Ahmed Arnob Md. Azmol Fuad Abu Tahir Nizam Arifin Tanjim Siam B. Computer Science 2021-09-07T14:17:38Z 2021-09-07T14:17:38Z 2021 2021-06 Thesis ID 17301145 ID 17301154 ID 17101393 ID 17301123 http://hdl.handle.net/10361/14986 en 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. 57 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Automatic License Plate Recognition (ALPR) Hough Line Transform YOLO Object Detection Fog Computing Optical Character Recognition (OCR) Computer Vision Data Traffic Management System (Computer system) |
| spellingShingle |
Automatic License Plate Recognition (ALPR) Hough Line Transform YOLO Object Detection Fog Computing Optical Character Recognition (OCR) Computer Vision Data Traffic Management System (Computer system) Arnob, Faed Ahmed Fuad, Md. Azmol Nizam, Abu Tahir Siam, Arifin Tanjim An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Islam, Md. Motaharul |
| author_facet |
Islam, Md. Motaharul Arnob, Faed Ahmed Fuad, Md. Azmol Nizam, Abu Tahir Siam, Arifin Tanjim |
| format |
Thesis |
| author |
Arnob, Faed Ahmed Fuad, Md. Azmol Nizam, Abu Tahir Siam, Arifin Tanjim |
| author_sort |
Arnob, Faed Ahmed |
| title |
An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| title_short |
An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| title_full |
An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| title_fullStr |
An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| title_full_unstemmed |
An efficient traffic management system to detect lane rule violation using Real-time Object Detection |
| title_sort |
efficient traffic management system to detect lane rule violation using real-time object detection |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14986 |
| work_keys_str_mv |
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