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computer-vision

License Plate Detection

3260 papers • 126 benchmarks • 313 datasets

License Plate Detection is an image-processing technology used to identify vehicles by their license plates. This technology is used in various security and traffic applications.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in license-plate-detection-1

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find license-plate-detection-1 models and implementations

Datasets

UFPR-ALPR

RodoSol-ALPR

SSIG-SegPlate

ChineseLP

Vehicle-Rear

Subtasks

No subtasks available.

Most implemented papers

Vehicle and license plate recognition with novel dataset for toll collection

Saeed Anwar, Muhammad Usama, Hafeez Anwar, Muhammad Shahid, A. Anwar, Helmuth Hlavacs•Thu Feb 10 2022

An automatic toll tax collection framework designed for challenging conditions, consisting of three sequential steps: vehicle type recognition, license plate localization, and license plate reading, using state-of-the-art YOLO models.

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Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

MSDA

MSDA

Caltech Cars

PKU (License Plate Detection)

PKU (License Plate Detection)

CLPD

CD-HARD

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A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Rayson Laroca, D. Menotti, W. R. Schwartz, G. Gonçalves, L. A. Zanlorensi, E. Severo, Luiz Oliveira•Sun Feb 25 2018

A robust and efficient ALPR system based on the state-of-the-art YOLO object detector is presented, employing simple data augmentation tricks such as inverted License Plates and flipped characters for character segmentation and recognition.

473 0

Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline

Zhenbo Xu, Wei Yang, Ajin Meng, Nanxue Lu, Huan Huang, Changchun Ying, Liusheng Huang•Fri Sep 07 2018

This paper presents a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy, and demonstrates the model outperforms current object detection and recognition approaches in both accuracy and speed.

280 0

Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs

Chunhua Shen, Hui Li•Wed Jan 20 2016

Inspired by the success of deep neural networks in various vision applications, DNNs are leveraged to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition.

217 0

License Plate Detection and Recognition in Unconstrained Scenarios

C. Jung, S. Silva•Fri Sep 07 2018

The main contribution is the introduction of a novel Convolutional Neural Network capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition (OCR) method to obtain the final result.

284 0

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

Rayson Laroca, D. Menotti, W. R. Schwartz, G. Gonçalves, L. A. Zanlorensi, E. Todt•Tue Sep 03 2019

An efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate detection and layout classification to improve the recognition results using post-processing rules is presented.

193 0

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

Rayson Laroca, D. Menotti, I. O. D. Oliveira, K. Fonseca, R. Minetto•Tue Nov 12 2019

This work introduces a novel dataset, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.

27 0

Towards end-to-end car license plate location and recognition in unconstrained scenarios

Shuxin Qin, Sijiang Liu•Mon Aug 24 2020

An efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously is presented, that is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time.

34 0

End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining

Songlu Chen, Shu Tian, Jia-Wei Ma, Qi Liu, Chun Yang, Feng Chen, Xu-Cheng Yin•Mon Oct 26 2020

A novel and applicable method for degraded license plate detection via vehicle-plate relation mining, which localizes the license plate in a coarse-to-fine scheme and predicts the quadrilateral bounding box in the local region by regressing the four corners of thelicense plate to robustly detect oblique license plates.

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Adding a benchmark result helps the community track progress.

License Plate Detection | State-of-the-Art