An efficient mobile augmented reality application for car bonnet component disparity check
- 0 Collaborators
Car bonnet parts consist of electrical and mechanical components that are highly complex in structure. Faults in the car bonnet parts may cause the entire system to fail. A mobile application with AR has been designed to make this process easier. ...learn more
Project status: Published/In Market
Intel Technologies
Intel Python,
Intel powered laptop
Overview / Usage
The proposed automated car engine disparity check system has been implemented using SIFT feature matching technique. Perfect car engine image is compared with the reference images. If there is any variation between the reference and test image, it is observed as fault and shall be highlighted. As of now, this is done for one specific model and has to be made compatible with all models through appropriate training.
Methodology / Approach
The input image is captured using digital camera under normal lighting condition which is converted into binary grayscale and is used in the program. The directory ‘Database’ is created and all the images are copied to that folder for processing. The user will have to takes images from ‘Database’ folder.
The perfect car engine image is identified as Reference image, this image is verified against the design rules and the standard specification . Through the algorithm developed, the comparison happens and immediately the disparity is highlighted.
Technologies Used
Python
Augmented Reality
Image processing
SIFT
SURF
Repository
https://ieeexplore.ieee.org/document/8305833