Edge Camera based Dynamic Lighting Control System for Smart Streetlights

Baek, Jang-Woon; Choi, Yun Won; Lee, Joon-Goo; Lim, Kil Taek

doi:10.1109/icaiic48513.2020.9065028

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Jang woon Baek et al [144] used Tiny-YOLO in edge cameras for object detection, dynamically adjusting SL brightness based on detected pedestrians and vehicles. Ren Tang et al [145] developed an intelligent dimming method specifically for intersections, using the YOLOv5s detection model.…”

Section: User-driven Controlmentioning

confidence: 99%

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Agramelal,

Sadik,

Moubarak

et al. 2023

Energies

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As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting.

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Section: User-driven Controlmentioning

confidence: 99%

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Agramelal,

Sadik,

Moubarak

et al. 2023

Energies

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show abstract

“…According to the literature, when using the camera to measure the illuminance in the environment, it is necessary to know the exposure time and gain in the camera, build a system calibration platform, calibrate the fixed parameters of the camera, and then calibrate the gray value of the digital image through the experimental platform. The relationship between the actual brightness of the environment [8][9] , at present, for the use of image sensors to measure the environmental illuminance, there are methods to obtain the brightness regression curve through least squares polynomial fitting and data linear fitting [10] . In actual application, the exposure The conditions and gains need to change with the light environment, so it is necessary to re-calibrate the relational coefficients between the image grayscale and the actual brightness through experiments, which causes great inconvenience in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Study on Illumination Measurement Method of Lighting Environment Based on RBF Neural Network

Zhang

2022

J. Phys.: Conf. Ser.

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In order to better measure the indoor light environment, evaluate the quality of the indoor lighting environment, and improve the requirements of lighting comfort, a camera image measurement illuminance method based on RBF(Radial basis function)neural network is proposed, and the camera sensor imaging theory is derived and analyzed to obtain the environmental illuminance and camera sensor The relationship between the parameters, the establishment of the RBF neural network model, by building an experimental system platform, collecting data sets to train the network model, fitting the neural network model parameters to obtain the illuminance measurement model, and use the image gray level and the reference point illuminance as the neural network Input and ambient illuminance as output. The prediction result shows that the error between the illuminance value predicted by the neural network and the actual measured value of the illuminance meter is within 10lx, and the relative error is less than 8%, which meets the requirements of lighting building design standards. Therefore, this method can achieve rapid measurement of the illuminance in the environment, and has a relatively high High precision.

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EdgeSL: Edge-Computing Architecture on Smart Lighting Control With Distilled KNN for Optimum Processing Time

et al. 2023

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Edge Camera based Dynamic Lighting Control System for Smart Streetlights

Cited by 6 publications

References 8 publications

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Study on Illumination Measurement Method of Lighting Environment Based on RBF Neural Network

EdgeSL: Edge-Computing Architecture on Smart Lighting Control With Distilled KNN for Optimum Processing Time

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