License Plate Detection
17 papers with code • 0 benchmarks • 10 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.
Benchmarks
These leaderboards are used to track progress in License Plate Detection
Datasets
UFPR-ALPR
SSIG-SegPlate
RodoSol-ALPR
ChineseLP
MSDA
Caltech Cars
PKU (License Plate Detection)
CD-HARD
CLPD
Vehicle-Rear
Most implemented papers
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
The best Mean Average Precision (mAP@0. 5) of 98. 8% for vehicle type recognition, 98. 5% for license plate detection, and 98. 3% for license plate reading is achieved by YOLOv4, while its lighter version, i. e., Tiny YOLOv4 obtained a mAP of 97. 1%, 97. 4%, and 93. 7% on vehicle type recognition, license plate detection, and license plate reading, respectively.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
ikigai-aa/Automatic-License-Plate-Recognition
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First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
detectRecog/CCPD
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ECCV 2018
Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets.
Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition.
License Plate Detection and Recognition in Unconstrained Scenarios
sergiomsilva/alpr-unconstrained
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ECCV 2018
Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e. g. European, US, Brazilian, Taiwanese, etc.
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents 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 (LP) detection and layout classification to improve the recognition results using post-processing rules.
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Then, a novel convolutional neural network branch is designed to further extract features of characters without segmentation.
End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates.
Iranis: A Large-scale Dataset of Farsi License Plate Characters
This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates.
