This project VLPR(Vehicle License PLate Recognition) recognize the License Number of cars in a picture using Deep Learning techniques. It takes input from a camera and sends it to control centre. there we apply Image processing and Deep learning techniques to indentifiy the license number of
Vehicle License Plate Recognition using Deep Learning Techniques
This project VLPR(Vehicle License PLate Recognition) recognize the License Number of cars in a picture using Deep Learning techniques.
It takes input from a camera and sends it to control centre. there we apply Image processing and Deep learning techniques to indentifiy the license number of vehicle in picture.
The main objective of this project is to improve vehicle related security with the help of AI. Sytems like this are already in market.We are trying to improve already implemented solution by:
1. Introducing more accurate system (above 80% accuracy in detecting number plate).
2. And to give cost effective solution.
Following are the step followed in implementation of this project.
1. Deciding the best Deep Learning algorith for this task.
2. Collecting and prepocessing of dataset.
3. Integrating Picamera with RaspberryPi and connection of on-site module with control center.
4. Training of Neural Network.
5. Implementing Neural Network , Image Processing techniques and testing.
Follwing are the benefits of this project:
1 This project helps in improving vehicle related security.
2. Low cost.
3. Improved accuracy.
Our final deliverable will be a complete system which can take picture and recognize license number of vehicles in that picture. Technical details of Final Deliverable includes:
1. An on-site module which will take picture and send it to control center.
2. Control centre where a computer system with gpu, which has a trained network in it will detect number plate from that image recieved.
3. In control center image goes throught thre main step Localization(using CNN), Segmentation(using Digital Image Processing) and Recognition(using DNN).
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| PiCam | Equipment | 1 | 1000 | 1000 |
| RaspberryPi | Equipment | 1 | 7000 | 7000 |
| Nvidia GeForce GTX 1080 | Equipment | 1 | 60000 | 60000 |
| Total in (Rs) | 68000 |
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