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DESIGN AND IMPLEMENTATION OF SMART
STREET LIGHTING SYSTEM USING
RASPBERRY PI
BY
T.U.NEHADHRUWA 193001069
S.ROHIT KUMAR 193001086
K.SRIHARINI 193001107
SRIKANTH.S 193001108
Department of Electrical and Electronics
engineering
SSN College Of Engineering.
GUIDED BY
Dr. M. BALAJI
Associate Professor
Department of EEE
SSN College Of Engineering
MOTIVATION
● Street lights has been one of the huge expenses of a city.
● Almost all the street lights are working throughout the night for about 12
hours per day.
● It is understood that some of the streets may not have vehicles or even
pedestrians passing throughout the night.
● In such scenarios, huge amount of electricity is wasted.
● This can be solved by the use of sensors/computer vision to sense the
surroundings and illuminate the street lights when necessary.
EXISTING METHODS AND THEIR DEMERITS
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Most of the existing street light systems are manually operated.
Smart street lights have only LDR sensors that helps us turn off street light
automatically during the day.
The proposed system uses LDR along with PIR sensor and computer vision to
turn ON the street lights only when traffic is detected.
In some of the existing systems of smart street lights, the street light is
switched on only when the vehicle passes right underneath it which makes it
dangerous and prone to accidents.
OBJECTIVES
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To implement smart street light system with the help of sensor and
computer vision.
To examine the pros and cons of both the methods.
To reduce power consumption by reducing the ON time of the street light
when not necessary.
To cut the operating costs of the street lights.
To provide security surveillance for better safety of the civilians.
WORKING - USING PIR SENSOR
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The brightness of the street is monitored using LDR sensor and when it
drops beyond threshold value, the traffic is detected using PIR sensor
placed on each street light.
When a movement is detected on the Nth lamp post, the (N+1)th street
light is turned ON.
The Nth light is turned on till the vehicle is detected at the (N+1)th lamp
post. By this process, the path of the vehicle is lit by switching ON the
street lights when necessary.
In case of more than one vehicle, we use the counter system.
WORK FLOW
DIAGRAM
BLOCK DIAGRAM – FOR SENSORS
WORKING – USING COMPUTER VISION
Finding Brightness using R-PI camera:
• We capture image of the street using camera module interfaced with the RPI.
• Then we convert the image from RBG(Red Blue Green) to HSV(Hue
Saturation Value) channels.
WORKING – USING COMPUTER VISION (CONTD)
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The value matrix of the image in HSV format indicates the brightness of
the image captured.
If the mean of value matrix is below a certain threshold, then we make use
of YOLO V3 (a real-time object detection algorithm) to detect
vehicles/pedestrians in the street.
Mean of value channel(Bright Image) – 170.8947
Mean of value channel(Dark image) – 57.40122
WORKING – USING COMPUTER VISION
(CONTD)
YOLO V3(You Only Look Once Version 3):
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YOLO V3 (You Only Look Once, Version 3) is a real-time object
detection algorithm that identifies specific objects in videos, live feeds,
or images.
YOLO V3 is trained using coco dataset.
YOLO V3 is capable of detecting 80 different objects including person,
bicycle, car, motorbike and bus. If a person/ vehicle is detected using
YOLO V3 model, then the street light is turned ON.
The pre-load weights of the YOLO V3 model is downloaded and made to
detect a person/vehicle in the street.
YOLO V3- CONTD
Why YOLO V3
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YOLO V3 even though has a
less accuracy compared to
YOLO V4 and YOLO V5, it
detects the objects way faster
than YOLO V4 and YOLO
V5(Only takes 22ms to detect
objects).
BLOCK DIAGRAM – FOR COMPUTER VISION
NOVELTY
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This project proposes a way to conserve power that is being consumed unnecessarily
by the street lights in the night.
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Currently, the streets have LDR sensors that help in reducing power consumed in the
day but it may not detect a pedestrian/vehicle which is standing still. This proposed
project addresses this problem by making use of PIR sensor.
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This is done by making use of PIR sensors to detect traffic density and raspberry Pi
with its camera module by capturing the live streaming of the street.
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It also addresses security surveillance across the streets making the data available in
the cloud so it can be accessed for various other purposes like detecting traffic and
immediate action against any crime taking place.
BUDGET
REFERENCES
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[1] Velaga, R. and Kumar, A. 2012. Techno-economic evaluation of the feasibility of a smart street system: A case study of
rural India. Procedia Social and Behavioral Sciences.62, 1220-1224.
[2] Echelon Corp.https://www.echelon.com/applications/street-lighting/
[3] Bruno, A., Di Franco, F. and Rasconà, G. 2012. Smart street lighting. EE Times http://www.eetimes.com/design/smartenergydesign/4375167/Smart-street-lighting.
[4] The e-JIKEI Network Promotion Institute, et al. Smart street light system with communication means. Published
unexamined patent application in Japan P2011-165573A (in Japanese).
[5] Redmon, J. &Farhadi, A. 8 April 2018. YOLOv3: An Incremental Improvement.
THANK YOU!!