4.2 Article

Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

Publisher

WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218488521400079

Keywords

Adaptive Neuro-Fuzzy Inference System; Bacterial Foraging Optimization (BFO); Hybrid Level Set Segmentation (HLSS); Magnetic Resonance Imaging (MRI); Content-Based Active Contour (CBAC); Particle Swarm Optimization (PSO)

Funding

  1. philosophy and social sciences research project of colleges and universities in Jiangsu Province [2019SJA1497]
  2. training plan of young and middleaged scientific research backbone of Nantong Institute of Technology [ZQNGG310]
  3. Nantong Guiding Science and Technology project [MSZ18012]
  4. Nantong Institute of Technology 2019 Classroom Revolution teaching reform research special project [2019JJG005]
  5. Nantong Institute of Technology 2017 excellent teaching team construction research project results [2017NITJXTD02]

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The Adaptive Intelligent Traffic Light control system utilizes OpenCV and image processing technology to provide timing for traffic light signals based on road traffic density, ensuring smooth traffic flow and reducing unnecessary green signal time.
The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.

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