SMART TRAFFIC CONTROL SYSTEM USING YOLO-MODEL
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- 1-5 Chapters
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- Recommended for : Student Researchers
- NGN 3000
Background of the study
Traffic congestion is a major problem in many cities, and the fixed-cycle light signal controllers are not resolving the high waiting time in the intersection. We see often a policeman managing the movements instead of the traffic light. He see roads status and decides the allowed duration of each direction. This human achievement encourages us to create a smart Traffic light control taking into account the real time traffic condition and smartly manage the intersection (Dimitrakopoulos et al., 2010). To implement such a system, we need two main parts: eyes to watch the real-time road condition and a brain to process it. A traffic signal system at its core has two major tasks: move as many users through the intersection as possible doing this with as little conflict between these users as possible. Video is a powerful medium for conveying information and data is plentiful wherever there are cameras (Lin et al., 2014). From dash, body, and traffic cams, to YouTube and other social media sites, there is no shortage of video data. Interesting applications that exploit this are ripe for development. One particularly compelling domain where video analytics has tremendous potential is in automated traffic control and public safety systems (Gajda et al., 2001).
1.2 Statement of the problem
The mere presence of automated traffic control systems, like red-light and speed cameras, has been shown to have a positive effect on the reduction of traffic violations. A worldwide analysis of 28 studies has concluded that such systems have reduced crashes by 8% to 50%. Carnis and Blais showed in their research that the initial response to France’s implementation of their automated speed enforcement program (ASEP) was a 21% reduction in fatal car accidents and a 26% reduction in non-fatal car accidents (Lin et al., 2014).
YOLO is a fast, accurate object detector. The biggest advantage of using YOLO is its superb speed – it's incredibly fast and can process 45 frames per second. YOLO also understands generalized object representation. This is one of the best algorithms for object detection and has shown a comparatively similar performance to the R-CNN algorithms (Lin et al., 2014). YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. As the name suggests, the algorithm requires only a single forward propagation through a neural network to detect objects (Dimitrakopoulos et al., 2010). This means that prediction in the entire image is done in a single algorithm run.
1.3 Objective of the study
The goal of this work is to improve intelligent transport systems by developing a Self-adaptive algorithm to control road traffic based on deep Learning. This new system facilitates the movement of cars in intersections, resulting in reducing congestion, less CO2 emissions, etc.Hence, the specific aim of this study is on smart traffic control system using YOLO-model.
1.4 Significance of the study
This study will enable transport and road management agencies to see the need to utilize YOLO-model in monitoring and controlling traffic on the road. Also, this study will serve as reference material for further studies.
1.5 Scope of the study
This study focuses on smart traffic control system using YOLO-model only.
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