عکس alitourani
A License-Plate detecttion application based on YOLOPython
موضوع‌ها
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فورک‌ها
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ستاره‌ها
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تاریخ ایجاد
۲۴ دی ۱۳۹۸
آخرین بروزرسانی
۱۰ ماه قبل
لایسنس
GNU General Public License v3.0

License plate Detection by YOLO

This repository contains a method to detect Iranian vehicle license plates as a representation of vehicle presence in an image. We have utilized You Only Look Once version 3 (YOLO v.3) to detect the plates inside an input image. The method has the advantages of high accuracy and real-time performance, thanks to YOLO v.3 architecture. The presented system receives a series of vehicle images and produces the processed image with added bounding-boxes containing the vehicles' license plates. The flow of how we have trained and tested the application is published in a paper accessible from the citation section.

Sample output of the system

🔨 Environment

  • Python v.3
  • You Only Look Once (YOLO) v.3
  • A vehicle image dataset containing 3000+ samples

💡 How to employ?

You can download the weights file from this link. It can also be downloaded from the weights folder (splitted files).

Test on a single image:

python object_detection_yolo.py --image=bird.jpg

Test on a single video file:

python object_detection_yolo.py --video=cars.mp4

Test on the webcam:

python object_detection_yolo.py

🧑‍💻 Contributers

🔗 Citation

Please cite the following paper in case you have used this repo:

@inproceedings{Khazaee2020,
    author = {Khazaee, Saeed and Tourani, Ali and Soroori, Sajjad and Shahbahrami, Asadollah and Suen, Ching Y.},
    title = {{A Real-Time License Plate Detection Method Using a Deep Learning Approach}},
    booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    doi = {10.1007/978-3-030-59830-3_37},
    isbn = {9783030598297},
    issn = {16113349},
    keywords = {Automatic number-plate detection,Deep learning,Image processing,Intelligent Transportation Systems},
    pages = {425--438},
    volume = {12068 LNCS},
    year = {2020}
}

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