hnjm/advanced-lane-detection

An advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding.

Basic Lane Detection

INTRODUCTION

Built an advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding. Identified lane curvature and vehicle displacement. Overcame environmental challenges such as shadows and pavement changes

MEDIUM ARTICLE

In this project, I have used computer vision techniques to identify lane boundaries and compute the estimate the radius of curvature given a frame of video of the road.

To achieve this, the following steps are taken:

• Computed the camera calibration matrix and distortion coefficients of the camera lens used given a set of chessboard images taken by the same camera
• Used the aforementioned matrix and coefficient to correct the distortions given by the raw output from the camera
• Use color transforms, and sobel algorithm to create a thresholded binary image that has been filtered out of unnecessary information on the image
• Apply perspective transform to see a “birds-eye view” of the image as if looking from the sky
• Apply masking to get the region of interest, detect lane pixels,
• Determine the best fit curve for each lane the curvature of the lanes
• Project the lane boundaries back onto the undistorted image of the original view
• Output a visual display of the lane boundaries and other related information

HOW TO USE

• You need to setup dependencies to run a Jupyter Notebook on your computer and setup a handful of packages such as opencv

import matplotlib.pyplot as plt
%matplotlib inline
import cv2 
import numpy as np
import pickle
from scipy.misc import imread

from moviepy.editor import VideoFileClip
from IPython.display import HTML

• To run any notebook properly, copy the jupyter notebooks from the /notebook folder to the root directory
• This is so that each notebook sees to see relevant files, the most relevant files being the python classes.

Relevant Links

• My Medium article
• • https://medium.com/@mithi/advanced-lane-finding-using-computer-vision-techniques-7f3230b6c6f2
• My submitted detailed technical writeup
• • https://github.com/mithi/advanced-lane-detection/blob/master/WRITEUP.pdf
• Udacity's project page
• • https://github.com/udacity/CarND-Advanced-Lane-Lines

RELEVANT FILES

Video Output

• project_video_output.mp4
• project_video_verbose_output.mp4

Pipeline

• pipeline.ipynb
• pipeline_verbose.ipynb

Classes

• ChessBoard - chessboard.py
• BirdsEye - birdseye.py
• LaneFilter - lanefilter.py
• Curves - curves.py