License Plate Detection via Information Maximization

Lee, Younkwan; Jeon, Jihyo; Ko, Yeongmin; Jeon, Moongu; Pedrycz, Witold

doi:10.1109/tits.2021.3135015

Cited by 9 publications

(8 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the results in Tab. 2, we conclude that the MA-CNN technique is competing with the [17,26] techniques where the best results (Bold) are alternating between the three techniques. Based on the AOLP experiment, the significant difference between the MA-CNN technique and [17] is the very high speed (120 FPS) compared to 16.6 FPS (for [17]) in the case of low resolution (320 × 240) and 92 FPS compared to 8.3 FPS in the case of the higher resolution (640 × 480).…”

Section: Experiments and Discussionmentioning

confidence: 89%

“…The results of the works [10][11][12][13][14] have been recorded as in [14]. In addition to the recorded results in [14], a recent work [26] on CCPD has achieved the highest precision based on an End-To-End deep learning architecture that included two faster-R-CNN subnetworks to detect both LP and non-LP text and used a loss function to prevent the detection of non-LP regions. Also, other data augmentation related to illumination complexities is needed to increase the accuracy of the CNN model because the BASE training set does not cover all the complexities included in the mentioned test sections.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Tab. 2 presents the results in case of the AOLP data set categories showing the work of my previous paper [17], the author of the dataset [25] and the recent work of [26]. Based on the results in Tab.…”

Section: Experiments and Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

Abosamra¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions. The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype. Using any of the previous versions will give moderate results but with very low speed. Hence, a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols. The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques. Also, a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns. Various image samples with a wide range of scale, orientation, and illumination conditions have been experimented with to verify the effect of the new improvements. Encouraging results with 97.55% detection precision have been reported using the recent challenging public Chinese City Parking Dataset (CCPD) outperforming the author of the dataset by 3.05% and the state-of-the-art technique by 1.45%.

show abstract

Section: Experiments and Discussionmentioning

confidence: 89%

Section: Experiments and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

Abosamra¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Y. Lee et al [1] focused on detection of license plates in wild (uncontrolled scenarios). They used deep learning techniques to perform ANPR.…”

Section: Overviewmentioning

confidence: 99%

Various License Plate Detection and Recognition Methods using Computer Vision and Machine Learning

Joshi¹,

Parikh

2023

ITM Web Conf.

View full text Add to dashboard Cite

With the increasing advancements in the technology, our lives have become significantly more convenient. We now have automated many things. One example of such things is the automated number plate recognition system. There are many ways to perform the ANPR (Automatic Number Plate Recognition). Performing ANPR in wild still remains a big challenge. This review focuses on some techniques that have tried to overcome this challenge.

show abstract

“…In addition, many other methods have focused on detecting license plates in complex scenes [43][44][45]. Several methods [46,47] attempted to exclusively handle the LPD problem in the wild. Nonetheless, all the above methods were specific for LPD but did not consider LPR.…”

Section: License Plate Detection (Lpd)mentioning

confidence: 99%

FAFEnet: A fast and accurate model for automatic license plate detection and recognition

Zhou

Cheng

Jiang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end-to-end CNN-based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN-based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real-world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency.

show abstract

License Plate Detection via Information Maximization

Cited by 9 publications

References 71 publications

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

Various License Plate Detection and Recognition Methods using Computer Vision and Machine Learning

FAFEnet: A fast and accurate model for automatic license plate detection and recognition

Contact Info

Product

Resources

About