Progress Made
My sole focus area this week was further Look Light traffic light detection cum Traffic Light Color algorithm. This image processing / pattern recognition based algorithm is based on the Hough transform, which returns the positions of circles of radii within a specified range as found within an input image. Because of the potential that the ML model would not be trained for the time being, I decided to work on using my existing algorithm to deduce the color of the light from the image of the whole scene as opposed to individual lights.
My approach was essentially applying my existing algorithm for each individual color filter (“red”, “green”, “yellow”), getting the number of circles identified and picking the color which resulted in the most circles.
Results:
- No significant change in accuracy *
- Increased latency (took 0.1-0.2 seconds to run for a full scene)
* On a side note, I was able to improve accuracy since the last status update by increasing the intensity of the median blur (larger kernel) to reduce the effect of noise that would be erroneously tagged as circular. (One example of such erroneous tagging, was the “detection” of circles in patches of leaves/shrubbery.)
With Yasaswini’s recent progress in her algorithm, my algorithm will no longer have to search for circles in the entire scene, as originally intended. I anticipate the accuracy to increase as I test my algorithm integrated with Yasaswini’s algorithm.
Progress
Progress made to actually output a color as opposed to a list of circles. Progress made to slightly improve accuracy. Seems like the right track. 🙂
Deliverables for Next Week
- Testing and further refinement with validation set traffic lights detected from Yasaswini’s algorithm, which she uploaded to the GitHub.