Overview / Usage

Object detection plays an integral role in the field of autonomous vehicle safety. The attempt I have made in this project is to develop a pipeline that can not only detect lane lines but also detect cars.

I do know the fact that state-of-the-art Deep Learning algorithms such as YOLO do exist but the motivation for this challenge was to develop a pipeline by implementing algorithms and using a non-typical Deep Learning approach. This allowed me to not only develop the math intuition behind these algorithms but also allow me to hardcode such algorithms instead of feeding the data straight into the neural network.

Methodology / Approach

The overall structure of the pipeline is as follows:

• 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.

Now that we’ve developed our lane detection algorithm, we’d now need to develop a pipeline for detecting cars and objects.

Here’s what our pipeline for this would look like:

• 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!

Technologies Used

Python, OpenCV, scikit-learn, matplotlib, time, pickle, moviepy, random

Repository

https://github.com/srianumakonda/Advanced-Lane-Detection-and-Object-Detection