Difference between revisions of "2019FallTeam6"

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(HSL Colors for Lane Detection)
(Methodology)
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# Training time
 
# Training time
 
# Completion of X clockwise laps on the indoor track
 
# Completion of X clockwise laps on the indoor track
 +
# Completion of X counterclockwise lap on the indoor track
 
# Completion of X clockwise laps on the outdoor track
 
# Completion of X clockwise laps on the outdoor track
# Completion of X counterclockwise lap on the indoor track
 
 
# Completion of X counterclockwise lap on the outdoor track
 
# Completion of X counterclockwise lap on the outdoor track
 
# Able to drive when bright or dark outside?
 
# Able to drive when bright or dark outside?

Revision as of 18:37, 2 December 2019

Introduction

Our project was to integrate OpenCV into donkeycar in order to try and improve training time and robustness of the model. We used OpenCV to pre-process images in order to extract edges and highlight the white and yellow lanes. We wanted to see if using those images as a combined 3-D image (edges, white lane, yellow lane) would improve training time and robustness of the model compared to the original donkeycar model which trained using RGB images.

Team Members

  • Cyrus Shen - ECE
  • Chenfeng Wu - ECE
  • Maximilian Stadler - CSE (UCSD Extension)
  • Isabella Franco - MAE

Mechanical Design

Camera Mount

Car Circuitry

ECE148 circuit.JPG

Integrating OpenCV

Lane Finding with OpenCV

HSL Colors for Lane Detection

In order to single out the white and yellow lanes from the rest of the image, we used HSL color-space to threshold the image for the colors white and yellow. The RGB image taken by the camera was converted to HSL using OpenCV's COLOR_RGB2HLS and then thresholded so that only white and yellow colors in the image were kept.

  • insert white lane image
  • insert yellow lane image
  • insert HSL info images

Canny Edge Detection

To outline the lanes, we used OpenCV's canny function which shows edges in an image.

  • insert canny edge image

Combined 3-D image

We combined the white, yellow, and canny edge images into a 3-D image and trained on that instead of the original RGB image.

  • insert new 3D image

Project

Methodology

In order to compare our OpenCV model to the current donkeycar model, we trained both models on the same images. 6 separate models were trained, 3 for an indoor track and 3 for an outdoor track. The 3 indoor track models were trained with 1000, 5000, and 10000 images of the indoor track with the car going clockwise. The same was done for the outdoor track model except the images were taken from the outdoor track.

We tested each model based on the following criteria:

  1. Training time
  2. Completion of X clockwise laps on the indoor track
  3. Completion of X counterclockwise lap on the indoor track
  4. Completion of X clockwise laps on the outdoor track
  5. Completion of X counterclockwise lap on the outdoor track
  6. Able to drive when bright or dark outside?

Indoor Track Model Results

insert video

Outdoor Track Model Results

insert video