In any driving scenario, lane lines are an essential component of indicating traffic flow and where a vehicle should drive. It’s also a good starting point when developing a self-driving car! Building on my previous lane detection project, I’ve implemented a curved lane detection system that works much better, and is more robust to challenging environments. The lane detection system was written in Python using the OpenCV library.
In my previous lane detection project, I’d developed a very simple lane detection system that could detect straight lane lines in an image. It worked decently under perfect conditions, however it would fail to detect curved lanes accurately, and was not robust to obstructions and shadows. This version improves upon both of these limitations.
Camera lenses distort incoming light to focus it on the camera sensor. Although this is very useful in allowing us to capture images of our environment, they often end up distorting light slightly inaccurately. This can result in inaccurate measurements in computer vision applications. However, we can easily correct this distortion.
How would you do this? You can calibrate your image against a known object, and generate a distortion model which accounts for lens distortions. This object is often an asymmetric checkerboard, similar to the one below:
See the rest of the project breakdown here!
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