Implementation of Machine learning algorithm like color Based segmentation for industrial application like Robotic vision , process control.

1. School of Electrical Engineering
Branch: Electronics And Telecommunication
Under Guidance of Prof. Bhairavi Sawant
Group ID: SEE07
Abhishek Sainkar TETB104 Exam Seat No:T187009
Omkar Rane TETB118 Exam Seat No:T187014
Kaustubh Wankhede TETB131 Exam Seat No:T187003

2. To Design And Develop Machine Vision Application based Object Detection
technique and implement it on Embedded Platform to provide imaging-based
automatic inspection and analysis for such applications as automatic inspection,
process control, and robot guidance, usually in industry.

3. ➢ O. Alhusain[05] discuss that In the food industry there are problems such as The first one is the decline in
food quality, and the second one is the “waste” problem associated with processing and preparation
operations. Hence, there is the need for quality inspection and assurance mechanisms to be installed in the
production lines of such mass food processing.
➢ Jarimopas et al. [06] have described development a machine vision experimentally sorting sweat
tamarinds parameter shape, size, colour and defects. It was performed with image processing software
analysis the image and hardware design include a conveyor, control drive light source, image antenna,
manage unit and micro-computer.
➢ Tushar Jain [07] described Machine vision provides innovative solutions in the direction of industrial
automation.These activities include, among others, delicate electronics component manufacturing, quality
textile production, metal product finishing, glass manufacturing, machine parts, printing products and
granite quality inspection, integrated circuits manufacturing and many others. Machine vision technology
improves productivity and quality management and provides a competitive advantage to industries that
employ this technology.
➢ Industries based on automation, consumer markets, medical domains, Défense and surveillance sectors are
most likely domains extensively using machine vision. Image classification, being the widely researched
area in the domain of computer vision has achieved remarkable results in world-wide competitions.

4. Food industry Manufacturing process in delicate Electronics
Industry.(Motherboard Manufacturing)
Medical Application in
Tumor or ucler detection

5. ➢ Detection of all objects in scope of vision.
➢ Gather Data in form of images.
➢ Train Model for detection of objects.
➢ Segmentation of objects according to user need.

6. For mini project we are restricting ourselves to detection of objects by camera and identifying the type of
objects. Object detection is one of major step in machine vision. Given a set of object classes, object
detection consists in determining the location and scale of all object instances, if any, that are present in
an image. Thus, the objective of an object detector is to find all object instances of one or more given
object classes regardless of scale, location, pose, view with respect to the camera, partial occlusions, and
illumination conditions.
In many computer vision systems, object detection is the first task being performed as it allows to obtain
further information regarding the detected object and about the scene. Once an object instance has
been detected (e.g., a face), it is be possible to obtain further information, including:
(i) to recognize the specific instance (e.g., to identify the subject’s face),
(ii) to track the object over an image sequence (e.g., to track the face in a video), and
(iii) to extract further information about the object (e.g., to determine the subject’s gender), while it is
also possible to:
(a) infer the presence or location of other objects in the scene (e.g., a hand may be near a face and at a
similar scale) and
(b) to better estimate further information about the scene (e.g., the type of scene, indoor versus outdoor,
etc.), among other contextual information.

8. Interfacing of Raspi Camera with camera
interfacing port
Board Raspberry pi 3 Model B
Processor Broadcom BCM2837
CPU Core Quadcore ARM Cortex-A53,
64Bit
Clock Speed 1.2GHz (Roughly 50% faster than
Pi2)
RAM 1 GB
GPU 400 MHz VideoCore IV®
Network Connectivity 1 x 10 / 100 Ethernet (RJ45 Port)
Wireless Connectivity 802.11n wireless LAN (WiFi)
and Bluetooth 4.1
USB Ports 4 x USB 2.0
GPIOs 2 x 20 Pin Header
Camera Interface 15-pin MIPI
Display Interface Display Interface
Power Supply (Current
Capacity)
2.5 A
External Memory SD Card Support for OS

10. SoC
Built specifically for the new Pi 3, the Broadcom BCM2837
system-on-chip (SoC) includes four high-performance ARM
Cortex-A53 processing cores running at 1.2GHz with 32kB Level
1 and 512kB Level 2 cache memory, a VideoCore IV graphics
processor, and is linked to a 1GB LPDDR2 memory module on
the rear of the board.
GPIO
The Raspberry Pi 3 features the same 40-pin general-purpose
input-output (GPIO) header as all the Pis going back to the
Model B+ and Model A+. Any existing GPIO hardware will work
without modification; the only change is a switch to which
UART is exposed on the GPIO’s pins, but that’s handled
internally by the operating system.

12. Size Around 25 × 24 × 9 mm
Weight 3g
Still resolution 5 Megapixels
Video modes
1080p30, 720p60 and 640 ×
480p60/90
Linux integration V4L2 driver available
C programming API OpenMAX IL and others available
Sensor OmniVision OV5647
Sensor resolution 2592 × 1944 pixels
Sensor image area 3.76 × 2.74 mm
Pixel size 1.4 µm × 1.4 µm
Optical size 1/4"
Full-frame SLR lens equivalent 35 mm
S/N ratio 36 dB
Dynamic range 67 dB @ 8x gain
Sensitivity 680 mV/lux-sec
Dark current 16 mV/sec @ 60 C
Well capacity 4.3 Ke-
Fixed focus 1 m to infinity
Focal length 3.60 mm +/- 0.01
Horizontal field of view 53.50 +/- 0.13 degrees
Vertical field of view 41.41 +/- 0.11 degrees
Focal ratio (F-Stop) 2.9
Picture formats
JPEG (accelerated), JPEG + RAW, GIF, BMP, PNG, YUV420,
RGB888
Video formats raw h.264 (accelerated)
Effects
negative, solarise, posturize, whiteboard, blackboard,
sketch, denoise, emboss, oil paint, hatch, gpen, pastel,
watercolour, film, blur, saturation
Exposure modes
auto, night, night preview, backlight, spotlight, sports,
snow, beach, very long, fixed fps, anti-shake, fireworks
Metering modes average, spot, backlit, matrix
Automatic white balance modes
off, auto, sun, cloud, shade, tungsten, fluorescent,
incandescent, flash, horizon
Triggers Keypress, UNIX signal, timeout
Extra modes
demo, burst/time-lapse, circular buffer, video with motion
vectors, segmented video, live preview on 3D models

13. Linux Based Distro
Operating System
Raspbian Stretch
with desktop and
recommended
software / 2019-04-
08
Open Source Machine
Learning Library for
Machine Learning
Application.
CV2
Programming
Language used
for coding 2.7NumPy is a library for the
Python programming
language, adding support for
large, multi-dimensional
arrays and matrices, along
with a large collection of high-
level mathematical functions
to operate on these arrays.

15. Step 1: Run shell Command python proj.py
Step 2: Import OpenCV2 library as CV2
Step 3: Import Numpy Library as np
Step 4: from picamera.array import PiRGBArray
Step 5: from picamera import PiCamera
Step 6: Create trackbar cv2.Trackbar() for R,G,B for colour Segmentation
values.
Step 7: Set Resolution of camera and frame rate for image specification.
camera.resolution = (640, 480)
camera.framerate = 30
Step 8:Capture Continuous Frames in Camera for Image Acquisition.
Step 9: Do Background Subtraction.
Step 10: Convert Red,Green and Blue to Hue Saturation Value.
Step 11: get lower and upper limit value from HSV values.
Step 12:Mask the values received from lower and upper limit of HSV.
Step 13: Close window .

16. Detection Of Red Color Bottle detection

17. Detection Of green Bottles

18. Blue Color bottle detection

22. In this project, an attempt was made to develop an object detection and tracking framework able to run in real-time on a Raspberry Pi 3
Model B. By using colour segmentation method, we were able to classify objects. This Project has future scope for implementation
such detection for industrial Automation and quality control practices.
OpenCV as the vision library of choice, is more than large and powerful enough to build a program of this type. The documentation
supporting the library is well written and easy to understand. The wealth of examples and explanations in the OpenCV community
have been able to answer any and all questions that arose during the development process, and has been invaluable as a source of
knowledge. The program has been developed, exclusively, using open source software. This points to the fact that further development
would most likely lead to a stable and cheap solution that could be incorporated into a commercial product, with no attached licensing
fees. The argument can also be made, concerning the hardware, that the Raspberry Pi, while being a good platform during
development, is too costly as the ultimate hardware in production. The hardware performance can definitely be lower, than what the
Raspberry Pi supplies, and still perform at a satisfying speed. Thus, cutting the cost of a final product even further.
In the future, we would make Real time object detection which will be able to distinguish them, also count the number of objects in
front.

23. [ 01 ] Bradski, G., Kaehler A. Learning OpenCV: Computer Vision in C++ with the OpenCV Library. Sebastopol,
California :O’Reilly Media,Inc.,2008.
[ 02 ] http://eie.uonbi.ac.ke/sites/default/files/cae/engineering/eie/Computer%20Vision%20through
%20the%20Raspberry%20PI%20Counting%20Objects.pdf
[ 03 ] https://www.pyimagesearch.com/2018/09/26/install-opencv-4-on-your-raspberry-pi/
[ 04 ] https://medium.com/nanonets/how-to-easily-detect-objects-with-deep-learning-on-raspberrypi-
225f29635c74
[ 05 ] O. Alhusain,VISION-BASED SYSTEM FOR QUALITY CONTROL OF SOME FOOD PRODUCTS.Wilson.,2003
[ 06 ] Jarimopas, Nitipong Jaisin, An experimental machine vision system for sorting sweet tamarind..2008,Elsevier
[ 07 ] Tushar Jain, Meenu. Automation and Integration of Industries through Computer Vision Systems..2013,
IRPHOUSE
[ 08 ] What is a Raspberry Pi?”. Raspberry Foundation, 2015. [Online]. Available:
https://www.raspberrypi.org/help/what-is-a-raspberry-pi/.
[ 09 ] https://www.rs-online.com/designspark/object-tracking-using-computer-vision-and-raspberry-pi
[ 10 ] https://www.hindawi.com/journals/tswj/2014/126025/