S16: Number 1

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Revision as of 22:14, 20 May 2016 by Proj user17 (talk | contribs) (Implementation)

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Grading Criteria

  • How well is Software & Hardware Design described?
  • How well can this report be used to reproduce this project?
  • Code Quality
  • Overall Report Quality:
    • Software Block Diagrams
    • Hardware Block Diagrams
      Schematic Quality
    • Quality of technical challenges and solutions adopted.

Project Title

Abstract

This section should be a couple lines to describe what your project does.

Objectives & Introduction

Show list of your objectives. This section includes the high level details of your project. You can write about the various sensors or peripherals you used to get your project completed.

Team Members & Responsibilities

  • YuYu Chen
    • OpenCV Programmer
  • Kenneth Chiu
    • Programmer/Video supplier
  • Thinh Lu
    • OpenCV Programmer
  • Phillip Tran
    • OpenCV Programmer

Schedule

Show a simple table or figures that show your scheduled as planned before you started working on the project. Then in another table column, write down the actual schedule so that readers can see the planned vs. actual goals. The point of the schedule is for readers to assess how to pace themselves if they are doing a similar project.

Week# Date Task Status Completion Date Description
1 3/21/2016 Get individual Raspberry Pi 2 for prototyping & research OpenCV on microcontroller Completed 3/20/2016
2 3/27/2016 Continue Research and learning OpenCV. Familiarize ourselves with API. Know the terminologies. Complete 4/2/2016
3 4/2/2016 Complete various tutorials to get a better feel for OpenCV. Discuss various techniques to be used. Complete 4/2/2016
4 4/4/2016 Research on lane detection algorithms. IE. Hough Lines, Canny Edge Detection, etc. Start writing small elements of each algorithm. Complete 4/8/2016
5 4/14/2016 Run sample codes on a still, non-moving picture. Complete 4/8/2016
6 4/20/2016 Write our own algorithm, and test it. Camera input, Proper Hough Line generation. Complete Included blurring, edge detection, and Hough transform. This is the base for the lane detection.
 Problem 1: A lot of noise being picked up by Hough transform.
 Status 1: Binarizing image seems to help decrease a lot of the noise. Gets rid of a lot of unnecessary objects
 Problem 2: Gaps in lanes do not complete Hough transform.
 Status 2: Currently working on Hough transform filters and Custom Region of Interest. Will possibly use birds-eye view.
7 4/22/2016 Modify the code for our own project objective. Include a "lane collider" to see when we cross a lane. Complete
8 5/2/2016 Implement the code with Raspberry Pi 2 Complete
9 5/8/2016 Testing in Lab and attach feedback system Complete
10 5/14/2016 Testing on the road Incomplete

Parts List & Cost

Raspberry Pi 2 - Given by Preet

Raspberry Pi camera - $20

Design & Implementation

The design section can go over your hardware and software design. Organize this section using sub-sections that go over your design and implementation.

Hardware Design

Discuss your hardware design here. Show detailed schematics, and the interface here.

Hardware Interface

In this section, you can describe how your hardware communicates, such as which BUSes used. You can discuss your driver implementation here, such that the Software Design section is isolated to talk about high level workings rather than inner working of your project.

Software Design

Below shows the basic algorithm on how lane detection can be achieved. The problem is that this will still produce a lot of noise, and intermediary steps must be done for proper lane detection.

S16 Number 1 Basic Algorithm.jpeg

(IN PROGRESS)


Implementation

This section includes implementation, but again, not the details, just the high level. For example, you can list the steps it takes to communicate over a sensor, or the steps needed to write a page of memory onto SPI Flash. You can include sub-sections for each of your component implementation.

Color Spaces

gray & hsv

IPM (Inverse Perspective Mapping)

IPM can be thought of as a "bird's eye view" of the road. IPM transforms the perspective of 3-D space into 2-D space, therefore removing the perspective effect. Using this method is advantageous as it focuses directly on the lane marking instead of the surrounding environment. Therefore, reducing a lot of noise.

 Mat findHomography(InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=noArray() )
 // can be used to transform perspective from original to IPM

ipm geometry

Although IPM was implemented and seemed promising, it was not used in the final version of the code. There was not enough time to further implement and transform/map the IPM coordinates back to the original video. Transforming IPM coordinates back to the original video can be done by:

 cv::getPerspectiveTransform(InputArray src, InputArray dst)   // to get the 3 x 3 matrix of a perspective transform
 cv::perspectiveTransform(inputVector, emptyOutputVector, yourTransformation)   // to transform the your IPM points to original video points

Although implemented and experimented, it was not able to map broken lane markings.

Contours

contour method, then switched to hough

Gaussian Blur


Canny Edge


Hough Line


Percentage offset


Switching Lanes

The typical time for an average driver to switch lane takes about one to three seconds. Using this time parameter, we can tell if the driver is switching lanes. The definition of switching lanes is when the driver departs from one lane boundary into another. When departing one lane boundary into another, only one lane will be detected by the camera. When only one lane is detected and within the time parameter, it will check if the lane marking is within a given boundary for it to be considered switching lanes.

Another method implemented to detect switching lanes is to measure the distance between the lane marking to the center of the vehicle (camera). As the distance decrease between a certain distance threshold and time, it will be considered switching lanes.

change lanes

Testing & Technical Challenges

Describe the challenges of your project. What advise would you give yourself or someone else if your project can be started from scratch again? Make a smooth transition to testing section and described what it took to test your project.

Include sub-sections that list out a problem and solution, such as:

Issue 1: Noise

There are Large amounts of noise when generating Hough lines. This is because there are lines in each picture. Standard Hough lines do not work because you cannot set the minimum and maximum lengths of lines. Probabilistic Hough Lines are the best bet for this task. Even with Probabilistic Hough Lines, there is still quite a bit of noise.

Solution:

We did not use Probabalistic Hough Line. We used the standard one. Instead of using the Gaussian Blur, converting the image to HSV and then binarizing(threshold) it was sufficient. Adjusting HSV and threshold allows us to just the lanes. This is not perfect because there will still be some noise. To filter out more of the noise, we filtered out unnecessary horizontal angles because lanes from the driver point of view, is not horizontal, but slanted vertically. We filtered between the angles of 15 and 60 degrees for the left lane, and 115 and 145 degrees for the right lane. Erosion was also used to make the lanes appear thinner so we could get a better average.

S16 Number 1 Hough Lines Noise.JPG

Issue 2: Broken Lane Markings

Conclusion

Conclude your project here. You can recap your testing and problems. You should address the "so what" part here to indicate what you ultimately learnt from this project. How has this project increased your knowledge?

Project Video

Upload a video of your project and post the link here.

Project Source Code

References

Acknowledgement

Any acknowledgement that you may wish to provide can be included here.

References Used

List any references used in project.

Appendix

You can list the references you used.