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

Xu, Zhenbo; Yang, Wei; Meng, Ajin; Lu, Nanxue; Huang, Huan; Ying, Changchun; Huang, Liusheng

doi:10.1007/978-3-030-01261-8_16

Cited by 255 publications

(264 citation statements)

References 31 publications

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“…In our experiments, we did not make use of two datasets proposed recently: AOLPE [26] (an extension of the AOLP dataset) and Chinese City Parking Dataset (CCPD) [54]. The former has not yet been made available by the authors, who are collecting more data to make it even more challenging.…”

Section: Datasetsmentioning

confidence: 99%

“…The former has not yet been made available by the authors, who are collecting more data to make it even more challenging. The latter, although already available, does not provide the position of the vehicles and the characters in its 250,000 images and it would be impractical to label them to train/evaluate our networks (Xu et al [54] used more than 100,000 images for training in their experiments).…”

Section: Datasetsmentioning

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)

419

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: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

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)

419

271

View full text Add to dashboard Cite

show abstract

“…The architecture of the proposed model SATN is illustrated in Fig. 2(b), the performance of LPR is improved by learning perspective-free and environment-independent semantic features of wild license plates, where we provide standard stencil-rendered license plates as prior knowledge with synthetic method [20]. SATN consists of four neural network components: 1) Dual attention transformation module A for perspective correction; 2) Feature encoder E for extracting environment-independent semantic features; 3) Discriminator D to classify the encoded features of wild and stencilrendered license plates; 4) Recognizer R to predict the license plate label.…”

Section: Shared Adversarial Training Networkmentioning

confidence: 99%

Robust License Plate Recognition With Shared Adversarial Training Network

et al. 2020

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Recently, deep learning has greatly promoted the performance of license plate recognition (LPR) by learning robust features from numerous labeled data. However, the large variation of wild license plates across complicated environments and perspectives is still a huge challenge to the robust LPR. To solve the problem, we propose an effective and efficient shared adversarial training network (SATN) in this paper, which can learn the environment-independent and perspective-free semantic features from wild license plates with the prior knowledge of standard stencil-rendered license plates, as standard stencil-rendered license plates are independent of complicated environments and various perspectives. Besides, to correct the features of heavily perspective distorted license plates perfectly, we further propose a novel dual attention transformation (DAT) module in the shared adversarial training network. Comprehensive experiments on AOLP-RP and CCPD benchmarks show that the proposed method outperforms state-of-the-art methods by a large margin on the LPR task. INDEX TERMS Deep learning, license plate recognition (LPR), dual attention transformation (DAT), shared adversarial training network (SATN).

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“…Finally, we recognize the LP number of the detected license plate. Generally, the LPR methods are divided into two types: one is the character segmentation method [17], and the other is the end-to-end method [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

Robust Automatic Recognition of Chinese License Plates in Natural Scenes

Peng

2020

IEEE Access

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Automatic license plate recognition has a wide range of applications in intelligent transportation systems and is of great significance. However, most of the current work on license plate recognition focuses on the images on the front of license plates. license plate recognition in natural scenes and arbitrary perspective is still a huge challenge. To solve this problem, this work mainly studies the detection and recognition of inclined Chinese license plates in natural scenes. We propose a robust method that can detect and correct multiple license plates with severe distortion or skewing in one image and input them into the license plate recognition module to obtain the final result. Different from the existing methods of license plate detection and recognition, our method performs affine transformation during license plate detection to rectify the distorted license plate image. It can not only avoid the accumulation of intermediate errors but also improve the accuracy of recognition. As an additional contribution, we put forward a challenging Chinese license plate recognition data set, including images obtained from different scenes under a variety of weather conditions. Through a large number of comparative experiments, we have proved the effectiveness of our proposed method.

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

Cited by 255 publications

References 31 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

Robust License Plate Recognition With Shared Adversarial Training Network

Robust Automatic Recognition of Chinese License Plates in Natural Scenes

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