A New CNN-Based Method for Multi-Directional Car License Plate Detection

Xie, Lele; Ahmad, Tasweer; Jin, Lianwen; Liu, Yuliang; Zhang, Sheng

doi:10.1109/tits.2017.2784093

Cited by 261 publications

(163 citation statements)

References 29 publications

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“…Xie et al [28] proposed a YOLO-based model to predict the LP rotation angle in addition to its coordinates and confidence value. Prior to that, another CNN was applied to determine the attention region in the input image, assuming that some distance will inevitably exist between any two LPs.…”

Section: License Plate Detectionmentioning

confidence: 99%

“…We consider LP recognition as the current bottleneck of ALPR systems since (i) impressive LP detection results have been reported in recent works [17,27,28], both in terms of recall rate and execution time; (ii) Optical Character Recognition (OCR) approaches must work as close as possible to the optimality (i.e., 100% of character recognition rate) in the ALPR context, as a single mistake may imply in incorrect identification of the vehicle [31]. Thus, in this work, we propose a unified approach for LP detection and layout classification in order to improve the recognition results using heuristic rules.…”

Section: Final Remarksmentioning

confidence: 99%

“…The AOLP dataset is categorized into three subsets, which represent three major ALPR applications: access control (AC), traffic law enforcement (LE), and road patrol (RP). As this dataset has been divided in several ways in the literature, we divided each subset into training and test sets with a 2:1 ratio, following [28,33]. Then, 20% of the training images were employed for validation.…”

Section: Evaluation Protocolmentioning

confidence: 99%

See 2 more Smart Citations

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)

433

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.

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Section: License Plate Detectionmentioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

Section: Evaluation Protocolmentioning

confidence: 99%

See 1 more Smart Citation

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)

433

271

View full text Add to dashboard Cite

show abstract

“…The overall accuracy of the proposed method was 93.84%. Xie et al [28] presented a novel convolutional neural network (CNN)based method for high-accuracy real-time car license plate detection. They proposed a CNN-based MD-YOLO (Multi-Directional "You Only Look Once") framework for multi-directional car license plate detection.…”

Section: Recent Workmentioning

confidence: 99%

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a comparative study of improvements to the algorithms for license plate extraction from images captured using conventional, modest-quality cameras. It compares the results and efficacy of two similar algorithms which primarily differ in the raw image pre-processing stage of the initial image. One algorithm uses the Gabor filter bank with distinctly crisp parameters; the other relies on the fuzzified Gabor filter bank with fuzzified parameters to facilitate their adjustment. Results indicate that the fuzzy reasoning introduced for Gabor filter parameter adjustment improves the detection of the components of interest in complex images and adds minimal deviation compared to the Gabor filter bank with distinctly crisp parameters.

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“…Al-Masni et al designed a YOLO-based malignant tumor learning model for breast cancer detection [14]. Xie et al developed a license plate detector [15]. Kang et al [16] improved YOLO for the winning solution in the 2017 Low-Power Image Recognition Challenge.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

Tung

Kelleher

Schlueter

et al. 2019

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

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Computer vision relies on labeled datasets for training and evaluation in detecting and recognizing objects. The popular computer vision program, YOLO ("You Only Look Once"), has been shown to accurately detect objects in many major image datasets. However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e.g. ambient lighting) changes. This paper describes a novel effort to evaluate YOLO's consistency for large-scale applications. It does so by working (a) at large scale and (b) by using consecutive images from a curated network of public video cameras deployed in a variety of real-world situations, including traffic intersections, national parks, shopping malls, university campuses, etc. We specifically examine YOLO's ability to detect objects in different scenarios (e.g., daytime vs. night), leveraging the cameras' ability to rapidly retrieve many successive images for evaluating detection consistency. Using our camera network and advanced computing resources (supercomputers), we analyzed more than 5 million images captured by 140 network cameras in 24 hours. Compared with labels marked by humans (considered as "ground truth"), YOLO struggles to consistently detect the same humans and cars as their positions change from one frame to the next; it also struggles to detect objects at night time. Our findings suggest that stateof-the art vision solutions should be trained by data from network camera with contextual information before they can be deployed in applications that demand high consistency on object detection.

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A New CNN-Based Method for Multi-Directional Car License Plate Detection

Cited by 261 publications

References 29 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

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

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