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https://github.com/ivangael/advanced-lanes-detection
An advanced lane detection system for improving vehicle safety
https://github.com/ivangael/advanced-lanes-detection
advanced-lane-finding advanced-lanes-detection opencv python ultralytics yolov8
Last synced: 5 days ago
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An advanced lane detection system for improving vehicle safety
- Host: GitHub
- URL: https://github.com/ivangael/advanced-lanes-detection
- Owner: IvanGael
- Created: 2024-07-23T21:10:15.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-15T18:27:11.000Z (about 1 month ago)
- Last Synced: 2024-10-17T03:18:51.830Z (about 1 month ago)
- Topics: advanced-lane-finding, advanced-lanes-detection, opencv, python, ultralytics, yolov8
- Language: Python
- Homepage:
- Size: 116 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Advanced Lane Detection
An advanced lane detection system using Using OpenCV, canny edge detector and hough transform algorithms
![Demo](demo.png)
The Project
---The steps of this project are the following:
* Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
* Apply a distortion correction to raw images.
* Apply a perspective transform to rectify binary image ("birds-eye view").
* Use color transforms, gradients, etc., to create a thresholded binary image.
* Detect lane pixels and fit to find the lane boundary.
* Determine the curvature of the lane and vehicle position to the center.
* Warp the detected lane boundaries back onto the original image.
* Lane Width: Computes the width of the lane based on polynomial coefficients.
* Get Top Down View of the lane using "birds-eye view" technique
* Vehicles Detection with YOLO11 by Ultralytics
* Distance Estimation of each Vehicle from others based on the centroids of the bounding boxesThe images for camera calibration are stored in the folder called `camera_cal`.
### Requirements
```bash
pip install -r requirements.txt
```### Run
```bash
py main.py --choice CHOICE --input INPUT_PATH
```