Year of seminar 2020

1. By,
SUHAIB. J
E7B
ROLL.NO 71
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2.  INTRODUCTION
 TYPES OF NIGHT VISION
 DIFFERENT GENERATIONS AND DEVICES
 APPLICATIONS AND SOME IMPLEMENTATIONS
 CHALLENGES OF PRESENT SYSTEM
 RECENT TRENDS AND FUTURE SCOPES
 REFERENCES
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3.  Night vision technology allows one to see in
the dark.
 Humans have very poor night vision
compared to animals.
 We can see a person standing over 183m
(200yards) in dark night using proper Night
vision device.
 Based upon the Technology used,it can be
classified as light intensification and thermal
imaging.
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4.  Image Intensification-This works by
collecting the tiny amounts of
light,intensifying it to visible range.
 Thermal Imaging -In this technique,captures
upper infrared light spectrum emitted by
objects.Different objects have different
temperature range of emission.
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5. A special tube, called an image-intensifier tube,
collects and amplifies infrared and visible
light.Requires light.
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8.  Generation 0 - based on image conversion,
rather than intensification. They required a
source of invisible infrared light mounted on
or near the device to illuminate the target
area.(1950s).
 Generation 1 - They have three image
intensifier tubes connected in a series.These
systems are larger and heavier than Gen 2
and Gen 3.
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9. GEN -1 NVD
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10.  Generation 2 – Second generation image
intensification significantly increased gain
and resolution by employing a microchannel
plate. A single electron entering a channel
initiates an avalanche process of secondary
emission, under influence of an applied
voltage, freeing hundreds of electrons.
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11. GEN – 2 NVD
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12.  Generation 3 - The photo cathode is made
using gallium arsenide, which is very efficient
at converting photons to electrons.High
working time.
 Generation 4 -The removal of the ion barrier
from the MCP that was added in Generation 3
technology reduces the background noise
and thereby enhances the signal to noise
ratio.
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13. Comparison of Performance with
respect to Gen 2
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14. Scopes for weapons
Surveillence Cameras
Goggles
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15. APPLICATIONS
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16.  The main problem during the night driving is
limited visibility of the road.
 Pedestrians using dark coloured clothes and
the inefficient lighting of vehicles cause false
interpretations.
 Computer vision-based solutions for the
driver assistance extend the capabilities of
vision for the driver and allow a real-time
response to occurring events.
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17.  This embedded system device is a relatively
low cost pedestrian detection method which
can be added into autombiles.
 Only some of the Premium vehicles have this
technology nowadays.
 This method comprises of three elements
Software layer,System layer and a Hardware
layer.
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20.  Thermal Camera – FLIR A325 sc KT-160
 Microcomputer – ODROID XU4 with Samsung
Exynos 5422 Octacore ,4 cortex A15(2GHz),4
Cortex A7(1.4GHz) and 2GB RAM.Graphics
processor MALI-T628 MP6 openGL ES
3.0/2.0/1.0
 Touch screen for ODROID - (800x480pixels)
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21.  Also UBUNTU version 16.04.3 ,C++ and
OpenCV library is needed in software side.
 The application can be developed in the IDE
Eclipse 9.3.1 environment and for the
implementation, the C++ programming
language can be used with the OpenCV 3.4.0
library.
 It would be impossible to illuminate an
infinite area during driving with any sort of
additional light source, since it could disturb
other traffic participants.
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22.  Main problem of this system is the difference
in temperature levels in pedestrians due to
metabolism,illness,drugs,etc.
 Detection techniques are of two types named
appearance based and model based.
 Temperature calibration in different times is a
negative influence of the thermal
detection,however possible changes are
estimated in prior to this method.
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23.  This work focus on a certain well-researched
approach, employing Haar-like features
combined with AdaBoost-based training
approach.
 Most of the detectors are not capable of learning
to detect objects that exhibit complex pose
variation or have a lot of other variability in how
they appear. To overcome this limitation, the
Convolutional Neural Networks were introduced,
which are capable of dealing with all these issues
within a single model.
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24.  By using neural network with Nvidia Jetson
platform, the extraction at severe conditions
can be done easily but the cost will be ten
times than ODROID platform.
 Different positions and distances based
detection are carried by ODROID platform.The
resolution of input greatly affects the
detection possibility.
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Samples with pedestrians approaching and
going far away of the camera.

26.  The reliability of device is also tested with
Convolutional Neural Network in ODROID
platform though the processing speed is less.
 The test result by the both algorithms are
given.We get a clear idea about the
effectiveness of both of them.
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29.  Haar+Adaboost Algorithm gives a satisfactory
performance when compared with cnn as cost
is concerned.
 The efficiency and accuracy can be increased
by means of effective algorithms like cnn with
high performance components.
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30.  High risk of damage to eye when used in
bright light conditions accidentally.
 Lack of colour vision.
 Night Vision Systems are costly.
 Decreased field of view.
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31.  Osprey X27 Colour Night vision Camera is a
great leap in this field developed by SPI in
LasVegas.
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32.  EZVIZ Inc produced first colour Night Vision
Surveillence Camera.
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33.  Low cost Night Vision Devices by means of
efficient sensors by extending to materials
that can operate in MWIR and LWIR regions of
spectrum and the algorithms for processing .
 Pulsed laser gated and time multiplexed
imaging to create 3D models of Environment.
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34. •Adam Nowosielski, Krzysztof Ma lecki, Pawel Forczmanski, Anton Smoli nski,
Kazimierz Krzywicki, ”Embedded Night-Vision System for Pedestrian Detection” in
proceedings of the IEEE,vol-20,issue-16, pp.9293-9304, August 2020.
DOI:10.1109/JSEN.2020.2986855
•YUXUAN XIAO, AIWEN JIANG,(Member, IEEE), JIHUA YE,AND MING-WEN
WANG, ”Making of Night Vision: Object Detection Under Low-Illumination”,
associated with IEEE,Vol-8, pp.123075-123086,July 2020.
DOI:10.1109/ACCESS.2020.3007610
•Yang Yang,Wenwen Zhang,Weiji He,Qian Chen and Guohua Gu “Research and
Implementation of Colour Night Vision imaging system based on FPGA and
CMOS”,Proc,SPIE 11434,2019 International Conference on Optical Instruments and
Technology:Optical Instruments and Modern Optoelectronic Instruments,
114340U(12March 2020);https://doi.org/10.1117/12.2548100
•Mohd Junedul Haque,Mohd Muntjir, ”Night Vision Technology:an Overview” in
accordance to International Journal of Computer Applications,Vol-167,no.13,
pp.0975-8887,June 2017, DOI:10.5120/ijca2017914562
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35. THANK
YOU!!
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