Rohan Kotwani developed an industrial image processing application using OpenCV and Python on a Raspberry Pi 2 camera to navigate a robot to detect and place colored blocks into corresponding bins for an IEEE robotics competition. The application used color thresholding, contour detection, and distance algorithms to identify colors, locate objects, and determine placement despite challenges with lighting, noise, and camera angles. Image data was sent via serial communication from the Raspberry Pi to an Arduino Mega controlling the robot.

1. OpenCV and Python
Industrial image processing application for IEEE Region 5 Robotics
Competition.
By Rohan Kotwani

2. IEEE Region 5 Robotics Competition
• The goal was to navigate to a color block & place that block into a bin
corresponding to the color.
• A pi camera with OpenCV installed was used for object detection,
navigation to object, and color detection.

3. Information Gathered & associated problems
• Color – The color detection was an issue different lighting conditions
effect the intensity of the color.
• Distance – The distance of the robot to the object primary depended
on the algorithm’s ability filter out noise & correct detect the
perimeter of the image
• Angle from center of camera – The angle was calculated using the
distance in pixels from the center of the image & the camera focal
length.

4. Parts List
• Raspberry Pi 2
• OpenCV
• Python 3
• Arduino Mega

5. How it was made
• Image processing was done on the R-Pi2, then the information was
sent via serial communication to the Arduino Mega.
• A “hand-shake” method was used to confirm the data transfer.

6. Exploratory threshold analysis
• A taxonomy of colors was created with a K-mean clustering algorithm.

7. Object detection
1. Color threshold filter
2. Contour detection
3. Algorithm to find contours of the right dimension

8. Distance detection
• The red points are the actual distances, the red line is the curve fitted
interpolation, and the blue points are the distance calculated with
triangle similarity.

9. Color detection
• The color was calculated by finding the average color inside contours.

10. The End