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Ubaid, MT, Saba, T, Draz, HU, Rehman, A, Ghani, MU and Kolivand, H (2022) Intelligent Traffic Signal Automation Based on Computer Vision Techniques Using Deep Learning. IT Professional, 24 (1). pp. 27-33. ISSN 1520-9202
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Abstract
Traffic congestion in highly populated urban areas is a huge problem these days. A lot of researchers have proposed many systems to monitor traffic flow and handle congestion through different techniques. But the current systems are not reliable enough to perceive traffic signals in real-time. Therefore, we aim to build a system that can efficiently perform real-time environments to solve the traffic congestion problem through signal automation. Since vehicle detection and counting are crucial in any traffic system, we use state-of-the-art deep learning techniques to detect and count vehicles in real-time. We then automate the signal timings by comparing the count of traffic on all sides of a junction. These automated signal timings sufficiently reduce congestion and improve traffic flow. We prepared a dataset of 4500 images and achieved about 91% accuracy by training it on Faster RCNN.
| Item Type: | Article |
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| Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | 0806 Information Systems |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Date Deposited: | 07 Mar 2022 10:14 |
| Last Modified: | 07 Mar 2022 10:15 |
| DOI or ID number: | 10.1109/mitp.2021.3121804 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/16452 |
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