Robust License Plate Recognition With Shared Adversarial Training Network

Zhang, Sheng; Tang, Guozhi; Liu, Yuliang; Mao, Huiyun

doi:10.1109/access.2019.2961744

Cited by 15 publications

(8 citation statements)

References 25 publications

(24 reference statements)

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“…CCPD & CLPD Table 3 shows the recognition performance on the CCPD-Base and CLPD datasets. We can see that our method equipped with ResNet-18 is very close to the state-of-the-art method in (Xu et al 2018;Zhang et al 2019) on CCPD-Base, and can get a higher accuracy using ResNet-101. As for CLPD, our proposed method remark-Method CCPD-base CLPD (Xu et al 2018) 98.50 66.5 (Zhang et al 2019) 99.00 - (Zhang et al 2020) 99…”

Section: Methodsmentioning

confidence: 64%

Efficient License Plate Recognition via Holistic Position Attention

Zhang

Wang

Zhuang

2021

AAAI

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License plate recognition (LPR) is a fundamental component of various intelligent transportation systems, and is always expected to be accurate and efficient enough in real-world applications. Nowadays, recognition of single character has been sophisticated benefiting from the power of deep learning, and extracting position information for forming a character sequence becomes the main bottleneck of LPR. To tackle this issue, we propose a novel holistic position attention (HPA) in this paper that consists of position network and shared classifier. Specifically, the position network explicitly encodes the character position into the maps of HPA, and then the shared classifier performs the character recognition in a unified and parallel way. Here the extracted features are modulated by the attention maps before feeding into the classifier to yield the final recognition results. Note that our proposed method is end-to-end trainable, character recognition can be concurrently performed, and no post-processing is needed. Thus our LPR system can achieve good effectiveness and efficiency simultaneously. The experimental results on four public datasets, including AOLP, Media Lab, CCPD, and CLPD, well demonstrate the superiority of our method to previous state-of-the-art methods in both accuracy and speed.

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Section: Methodsmentioning

confidence: 64%

Efficient License Plate Recognition via Holistic Position Attention

Zhang

Wang

Zhuang

2021

AAAI

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“…An efficient shared adversarial training network has been proposed for plate detection in [32]. This model can learn environment independent semantic features without perspective from real plates using prior knowledge of standard plates.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

An End-to-End Deep Learning Approach for Plate Recognition in Intelligent Transportation Systems

Pirgazi

Kallehbasti

Sorkhi

2022

Wireless Communications and Mobile Computing

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Accurate and fast recognition of license plates is one of the most important challenges in the field of license plate recognition systems. Due to the high frame rate of surveillance cameras, old license plate recognition systems cannot be used in real-time applications. On the other hand, the presence of natural and artificial noise and different light and weather conditions make the detection and recognition process of these systems challenging. In this paper, an end-to-end method for efficiently detecting and recognizing plates is presented. In the proposed method, vehicles are first detected using a single-shot detector- (SSD-) based deep learning model in the video frames and the input images. This will increase the speed and accuracy in identifying the location of the plate in the given images. Then, the location of the plate is identified using the proposed architecture based on convolutional networks. Finally, using a deep convolutional network and long short-term memory (LSTM), the characters related to the plate are recognized. An advantage of our method is that the proposed deep network is trained using different images with different qualities that leads to high performance in detecting and recognizing plates. Also, considering that in the proposed method the vehicles are first detected and then the plate is detected in the vehicle image, there is no limit in the number of identified plates. Moreover, plate detection in the vehicle rectangle, instead of the whole frame, speeds up our method. The proposed method is evaluated using several databases. The first part of the evaluation focuses on robustness and recognition speed. The proposed method has the accuracy of 100% for vehicle detection, 100% for plate detection, and 99.37% for character recognition. In the second part of evaluation, the proposed method is evaluated in terms of overall speed. The experimental results witness that the proposed method is capable of processing 30 frames per second without losing any data and also outperforms several methods proposed in recent years, in terms of time and accuracy.

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“…In [15, 16], long short‐term memory (LSTM) net is trained to recognise the sequential features extracted from the whole license plate via CNN. In addition, a shared adversarial training network is proposed by Zhang et al [17] to promote the performance of bidirectional LSTM. Generally, RNNs‐based methods depend on the computations of the previous time step and therefore do not allow parallel processing over every element in a sequence.…”

Section: Related Workmentioning

confidence: 99%

“…The recognition problems can be transformed into sequence labelling problems. To solve this type of problems, CNN-based simple parallel classification methods [10][11][12][13] and recurrent neural network (RNN) methods [14][15][16][17] are usually adopted to label the license characters directly. The simple parallel classification methods are just designed for fixed number of characters, which limits their application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Efficient and unified license plate recognition via lightweight deep neural network

Qin¹,

Liu

2020

IET image process

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In this study, the authors are interested in building a unified deep learning framework to solve the recognition problem of both single-line and double-line car license plates. Most existing methods are designed for single-line license plate recognition. For double-line cases, detection and segmentation are usually adopted firstly to locate each line of characters. These methods are usually environmentally sensitive and will bring propagation of error between segmentation and recognition. To solve the problems, the authors propose a unified method that can recognise both single-line and double-line license plates in an end-to-end way without line segmentation and character segmentation. Specifically, an improved lightweight convolutional neural network is used to extract features efficiently. Then, the multi-task learning strategy is used to simultaneously perform license plate classification and character recognition. Finally, recognition task is treated as sequence labelling problems, which are solved by connectionist temporal classification directly. Experimental results indicate that the proposed method significantly outperforms the previous state-of-the-art methods on both public datasets and the synthetic dataset.

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Robust License Plate Recognition With Shared Adversarial Training Network

Cited by 15 publications

References 25 publications

Efficient License Plate Recognition via Holistic Position Attention

Efficient License Plate Recognition via Holistic Position Attention

An End-to-End Deep Learning Approach for Plate Recognition in Intelligent Transportation Systems

Efficient and unified license plate recognition via lightweight deep neural network

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