ROAD
R.O.A.D is a desktop application which will provide a deeper insight to image processing, computer vision and will enabling different aspects of lane detection and improving the results using ML. Humans while driving have a disadvantage of not always being attentive (whether it be changing th
Project Title
ROAD
Project Area of Specialization
Project Summary
R.O.A.D is a desktop application which will provide a deeper insight to image processing, computer vision and will enabling different aspects of lane detection and improving the results using ML.
Humans while driving have a disadvantage of not always being attentive (whether it be changing the radio or of being tired etc.) while a computer, if trained, can always be fully attentive at detecting lanes.
Project Objectives
R.O.A.D will collect the data from the mounted front view camera and applying image processing and object detection to distinguish lanes, vehicles and alert the driver. R.O.A.D will provide a deeper insight to image processing and computer vision and enabling different aspects of lane detection and improving the results. The end goal objective of this project is to:
- Train and process different data sets for lane detection.
- Improving the image processing and lane detection process.
- Providing real time lane and object detection.
Project Implementation Method
R.O.A.D will collect the data from the mounted front view camera and applying image processing and object detection to distinguish lanes, vehicles and alert the driver. The overall scope of the project includes
- Image capturing
- Image processing(edge detection, noise redution)
- Python(openCV, numpy)
Then the preprocessed frames will be fed to a trained model which will implement lane detection.
Output with detected road and lanes will be shown.
Benefits of the Project
It is mainly needed by anyone who drives. As it would benefit those who are not attentive while driving as it would detect lanes on the road.
Technical Details of Final Deliverable
A desktop application will be made using pyGUI or Tkinter (a python based GUI framework).
It will prompt the user to either upload a prerecorded video (for simplification or take live input [extended scope]). Once the video is uploaded or live video captured, the preprocessing stage begins which breaks the video feed into frames and those frames fed to a trained machine learning model which detects lanes on the road.
It will make use of python based frameworks for image processing like openCV, tensor flow, numpy and pandas.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Dashboard Camera | Equipment | 1 | 20000 | 20000 |
| GPU | Equipment | 1 | 25000 | 25000 |
| Printing | Miscellaneous | 1 | 500 | 500 |
| Overheads | Miscellaneous | 1 | 500 | 500 |
| Power Bank | Equipment | 1 | 5000 | 5000 |
| Total in (Rs) | 51000 |
If you need this project, please contact me on contact@adikhanofficial.com
Smart Security System using IoT for Industry and Warehouses
Security has become one of the main concerns nowadays. It is very important for all the of...
Ghoomlo
Our tourist recommended system app will provide a tour guide to the traveller who wants to...
Design, Analysis & manufacturing of Thermal power fluctuations dam...
Waste heat is the energy that is not utilized and lost in the environment. It can be...
Automated paint mixer
An automated paint mixing machine that uses RGB colors to provide the required result with...
Smart Atmospheric water generator for drinking water
Scarcity of water remains the ever green concern to the whole world. The water in the atmo...