Pairing Lane Detection with Object Detection


Lane Detection Pipeline

  • Calibrate the camera using a chess/checkerboard to prevent distortion.
  • Apply a distortion correction to these images.
  • Use colour transforms to create a binary image.
  • Apply a perspective transform to get a “birds-eye view”
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to the center.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

Calibrate the camera using a chess/checkerboard to prevent distortion.

Input, raw image of the camera taking a picture of a chessboard.
Here’s how the camera looks after we undistort it.
Input image
Output image, after undistortion

Creating a binary image using advanced colour transformation techniques

Perspective transformation — getting that bird's eye view

Input image
Trapezoidal image

The fun part — Lane Line Detection

Input image
We’ve gotten some really smooth, beautiful lines!!

Boom! Lane Detection

Part 2: Object Detection

  • Preprocess the data and extract the features from it
  • Build an AI model that can detect cars vs not a car
  • Create a sliding window algorithm that’ll slide across the image and make predictions
  • Create a heatmap for false positives
  • Limit the false positives by merging them into 1 collective prediction
  • Merge them all together and get our final object detection pipeline!

Data preprocessing

Building an AI model that can detect cars

Creating a sliding window for object detection

Credit: Udacity
Examples of car detection given this input image

False positives

Obviously, there is room for improvement but not bad!

The final part: Putting this all together




Building Self-Driving Cars as a 15yo.

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Sri Anumakonda

Sri Anumakonda

Building Self-Driving Cars as a 15yo.

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