License plate detection based on fully convolutional networks

Han, Xiang; Yuan, Yule; Zhao, Yong; Fu, Zufeng

doi:10.1117/1.jei.26.5.053027

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…We used 80 images of the Caltech Cars dataset for training and 46 for testing, as in [55][56][57]. Then, we employed 16 of the 80 training images for validation (i.e., 20%).…”

Section: Evaluation Protocolmentioning

confidence: 99%

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

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

413

271

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In this paper, we present an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the stateof-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. The system is conceived by evaluating and optimizing different models with various modifications, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.8% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

show abstract

“…We used 80 images of the Caltech Cars dataset for training and 46 for testing, as in [55][56][57]. Then, we employed 16 of the 80 training images for validation (i.e., 20%).…”

Section: Evaluation Protocolmentioning

confidence: 99%

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

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

413

271

View full text Add to dashboard Cite

show abstract

“…We used 80 images of the Caltech Cars dataset for training and 46 for testing, as in [67][68][69]. Then, we employed 16 of the 80 training images for validation (i.e.…”

Section: Evaluation Protocolmentioning

confidence: 99%

An efficient and layout‐independent automatic license plate recognition system based on the YOLO detector

Laroca

Zanlorensi

Gonçalves

et al. 2021

IET Intelligent Trans Sys

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This paper presents an efficient and layout‐independent Automatic License Plate Recognition (ALPR) system based on the state‐of‐the‐art you only look once (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. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade‐off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end‐to‐end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR‐EU, SSIG‐SegPlate and UFPR‐ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. The authors' system also achieved impressive frames per second (FPS) rates on a high‐end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that the authors manually labelled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

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“…In [ 14 ], a modified template-matching algorithm is presented through color pixel analysis. In [ 15 ], a fully Convolutional Neural Network (CNN) was deployed to extract features at different stages of the CNN to differentiate the details of the plates and the background followed by a three loss layer architecture for accurate plate detection.…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic License Plate Detection

Mahmood

Khan

et al. 2022

Sensors

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Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

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License plate detection based on fully convolutional networks

Cited by 15 publications

References 32 publications

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

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

An efficient and layout‐independent automatic license plate recognition system based on the YOLO detector

Towards Automatic License Plate Detection

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