2025-09-03 –, B07-B08
Are you a scientist or a developer looking to understand how to use NumPy to solve computer vision problems?
NumPy is a Python package that provides the multidimensional array object which you can use to solve the lane detection problem in computer vision for self-driving cars or autonomous driving. You can apply non-machine learning techniques using NumPy to find the straight lines on street images. No other external libraries, just python with NumPy.
Car accidents happen every day. Lane detection is a common use case in computer vision and self-driving which can help prevent accidents by identifying the boundaries of driving lanes on an image of a road scene. It is a fundamental requirement for autonomous vehicles to drive safely on the roads.
In my talk, “Lane detection in self-driving using only numpy”, I will share my learning experience on how to solve a computer vision problem with a small dataset.
Outline of my talk: 25-27 minutes
2 minutes - the problem
5 minutes - What is NumPy and the steps of the algorithm, & the methods (Create Arrays using NumPy functions, Array slicing, indexing, math using NumPy Broadcasting)
10 minutes - edge detection algorithms
10 minutes - ROI extraction, hough transform and detecting the lines
None
Prerequisites:Basic knowledge of Python and NumPy.
Abstract as a tweet (X) or toot (Mastodon):My learning experience on how to solve a computer vision problem with NumPy and a small dataset.
Notes for reviewers only:I've chosen Computer Vision as the track, but it can also fit into Scientific Python because of NumPy.
Emma Saroyan is a developer advocate and an author. She has worked in startups at the intersection of technology and education, and is a passionate community builder within the open source ecosystem. Emma is running a local Postgres User Group in Yerevan, Armenia, where she organizes meetups and conferences. She currently serves on Diversity & Inclusion Committees of PgUS and NumFOCUS. She enjoys giving talks, and sharing her knowledge at tech conferences.