Unified Chinese License Plate detection and recognition with high efficiency

Gong, Yanxiang; Deng, Linjie; Tao, Shuai; Lu, Xinchen; Wu, Peng; Xie, Zhihua; Ma, Zheng; Xie, Mei

doi:10.1016/j.jvcir.2022.103541

Cited by 33 publications

(6 citation statements)

References 29 publications

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“…In this paper, we also compare with other methods on the CCPD-2019 dataset. Since [7] and [9] have both evaluated their methods under different circumstances, in order to make a fair comparison, this paper conduct experiments on various test subsets of CCPD-2019. According to the experiments of [7] and [9], Cascade classifier [20], SSD300 [21], YOLO9000 [22], Faster-RCNN [23], YOLOv4 [13], SYOLOv4 (Scaled YOLO-v4) [24], and STELA [25] are used as the detection model.…”

Section: Results Analysismentioning

confidence: 99%

“…Since [7] and [9] have both evaluated their methods under different circumstances, in order to make a fair comparison, this paper conduct experiments on various test subsets of CCPD-2019. According to the experiments of [7] and [9], Cascade classifier [20], SSD300 [21], YOLO9000 [22], Faster-RCNN [23], YOLOv4 [13], SYOLOv4 (Scaled YOLO-v4) [24], and STELA [25] are used as the detection model. HC (Holistic-CNN) [26] and CRNN [27] are used as recognition models, and the end-to-end methods TE2E [28] and RPNet [7] are used for comparison.…”

Section: Results Analysismentioning

confidence: 99%

“…Dataset Overview The ability of LP recognition depends on the quality of model training, which relies heavily on datasets. Due to the small scale [8,9], unbalanced LP type and character distribution [7] in the existing LP datasets, it is difficult to train a robust Chinese LP recognition model to solve the LP recognition task in the open scene. In view of this phenomenon, this paper makes a series of matching Chinese LP datasets, which includes simulated LP subset, real-time monitoring of moving vehicle LP subset, character-level annotation subset, simulated character subset, and single-character segmentation classification subset.…”

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confidence: 99%

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High Precision License Plate Recognition Algorithm in Open Scene

Shu

2023

J. Phys.: Conf. Ser.

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At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result. On the basis of the above, a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters. At the same time, in order to meet the needs of character detection and character classification, as well as the imbalance of the existing license plate datasets, this study contributed a series of supporting license plate datasets. According to the published publications, this study contributed the first large-scale character-level annotated license plate dataset. Extensive experiments show that the method in this study can reach the current state-of-the-art on different datasets. If accepted, the dataset will be publicly available at https://gitee.com/wust30405/lpdataset.

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Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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High Precision License Plate Recognition Algorithm in Open Scene

Shu

2023

J. Phys.: Conf. Ser.

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“…The research mentioned above highlights that, under ideal conditions and specific circumstances, license plate recognition demonstrates commendable accuracy. However, existing methods falter when confronted with multiple complex real-world traffic scenarios, resulting in subpar license plate recognition performance [9]. For instance, factors such as deteriorating image quality due to weather changes, uneven license plate brightness caused by lighting conditions, poor dataset quality, blurriness in license plate quality due to high vehicle speeds, and license plate deformation caused by varying capture angles present considerable challenges to both the robustness and accuracy of recognition processes [10].…”

Section: Introductionmentioning

confidence: 99%

EAND-LPRM: Enhanced Attention Network and Decoding for Efficient License Plate Recognition under Complex Conditions

Chen,

Li,

et al. 2024

Algorithms

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With the rapid advancement of urban intelligence, there is an increasingly urgent demand for technological innovation in traffic management. License plate recognition technology can achieve high accuracy under ideal conditions but faces significant challenges in complex traffic environments and adverse weather conditions. To address these challenges, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model leverages an encoder to extract features from image sequences and employs a self-attention mechanism to focus on critical feature information, enhancing its capability to handle complex traffic scenarios such as rainy weather and license plate distortion. We have curated and utilized publicly available datasets that closely reflect real-world scenarios, ensuring transparency and reproducibility. Experimental evaluations conducted on these datasets, which include various complex scenarios, demonstrate that the EAND-LPRM model achieves an accuracy of 94%, representing a 6% improvement over traditional license plate recognition algorithms. The main contributions of this research include the development of a novel attention-mechanism-based architecture, comprehensive evaluation on multiple datasets, and substantial performance improvements under diverse and challenging conditions. This study provides a practical solution for automatic license plate recognition systems in dynamic and unpredictable environments.

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“…The conventional object detection and recognition methods have recently been replaced by neural networks based deep learning techniques in the computer vision domain. This has resulted in improved accuracy and the ability to recognize license plates in more challenging environments [3][4].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Training and Testing Datasets for License Plate Recognition

Khan¹,

Alshomrani²,

Khan³

et al. 2022

IJACSA

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Modern society has made tremendous progress towards automation to increase the quality of life and reduce the margin of human error. Intelligent transportation systems are a critical aspect of this evolution. The core technology of these systems is the automatic identification of vehicles' license plates to monitor safety and control violations of traffic rules and other crimes. The research on license plate detection and recognition has gone a long way, from traditional computer vision techniques to features (color, shape, text, etc.) based classification and finally to modern deep learning structures. The deep networks comprising hundreds of layers require enormous amounts of training data. The training dataset should contain plates from different countries; otherwise, the system will be specific to only certain types of plates (from a country or province). There are several datasets collected by researchers containing large numbers of license plates from different countries. This paper provides a detailed survey of such datasets available in the public domain. Sample images from each dataset are shown, and details such as the dataset size, size of images, download link, and country of origin are provided. This survey will be a helpful reference for new researchers in the field for the tasks of training new networks and benchmarking their performances.

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Unified Chinese License Plate detection and recognition with high efficiency

Cited by 33 publications

References 29 publications

High Precision License Plate Recognition Algorithm in Open Scene

High Precision License Plate Recognition Algorithm in Open Scene

EAND-LPRM: Enhanced Attention Network and Decoding for Efficient License Plate Recognition under Complex Conditions

Deep Neural Network Training and Testing Datasets for License Plate Recognition

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