research-article

Enhancing YOLO deep networks for the detection of license plates in complex scenes

Authors:

Rabiah Al-qudah,

Ching Y. SuenAuthors Info & Claims

DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems

Article No.: 21, Pages 1 - 6

https://doi.org/10.1145/3368691.3368712

Published: 02 December 2019 Publication History

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NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the DATA 2019 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

Abstract

License plate detection (LPD) in context is a challenging problem due to its sensitivity to environmental factors (such as rain, dust, and shadow) and light, which may greatly influence the detection accuracy. Moreover, LPD is more challenging for real-time systems. The usage of deep learning attracted the attention of researchers in recent years. It is being widely employed to solve classification and detection problems. Recently, for object detection, a new deep network was developed, namely, You Only Look Once (YOLO).

We propose a YOLO-inspired adaptive solution with optimized parameters to enhance detection performance. To improve the detection process, the proposed solution employs "model generator" and "testing configurator" components where each model is trained using one single deep network. In addition to testing the newly designed solution using the UFPR-ALPR dataset, this work introduces a new annotated dataset for Canadian license plates (LP), namely CENPARMI datasets. The newly introduced dataset is challenging as it contains images with different settings in terms of: brightness, skewing, and distance. In addition to reporting the recall ratio results, a detailed error analysis to provide some insights into the types of false positives has been conducted. The proposed solution choice of optimal parameters enhanced the recall ratio and precision. For example, the proposed solution improved the recall ratio from 84.4% to 98.3% and the precision from 65.37% to 89.17% when tested using the UFPR-ALPR dataset.

References

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Vargoorani ZSuen C(2024)License Plate Detection and Character Recognition Using Deep Learning and Font EvaluationArtificial Neural Networks in Pattern Recognition10.1007/978-3-031-71602-7_20(231-242)Online publication date: 19-Sep-2024
https://doi.org/10.1007/978-3-031-71602-7_20
Ma JYan WLiu GXing SNiu SWei T(2022)Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO AlgorithmAdvances in Civil Engineering10.1155/2022/78793022022:1Online publication date: 23-Aug-2022
https://doi.org/10.1155/2022/7879302
Mohanty AA BShaik A(2022)A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)10.1109/ICSTCEE56972.2022.10100084(1-5)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICSTCEE56972.2022.10100084
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DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems

December 2019

376 pages

ISBN:9781450372848

DOI:10.1145/3368691

Conference Chair:
Imad Hoballah
AUD, UAE
,
Organizing Chair:
Hassan Zeineddine
AUD, UAE
,
Program Chairs:
Shadi A. Aljawarneh
JUST, Jordan
,
Juan. A. Lara
UDIMA, Spain

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Published: 02 December 2019

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DATA'19

DATA'19: International Conference on Data Science, E-learning and Information Systems 2019, Sponsored by UAE ACM Chapter

December 2 - 5, 2019

Dubai, United Arab Emirates

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Vargoorani ZSuen C(2024)License Plate Detection and Character Recognition Using Deep Learning and Font EvaluationArtificial Neural Networks in Pattern Recognition10.1007/978-3-031-71602-7_20(231-242)Online publication date: 19-Sep-2024
https://doi.org/10.1007/978-3-031-71602-7_20
Ma JYan WLiu GXing SNiu SWei T(2022)Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO AlgorithmAdvances in Civil Engineering10.1155/2022/78793022022:1Online publication date: 23-Aug-2022
https://doi.org/10.1155/2022/7879302
Mohanty AA BShaik A(2022)A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)10.1109/ICSTCEE56972.2022.10100084(1-5)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICSTCEE56972.2022.10100084
Almuhajri MSuen C(2022)A Complete Framework for Shop Signboards Detection and Classification2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956399(4671-4677)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956399
Almuhajri MSuen C(2022)Shop Signboards Detection Using the ShoS DatasetPattern Recognition and Artificial Intelligence10.1007/978-3-031-09282-4_20(235-245)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-09282-4_20
Chen SLiu QMa JYang C(2021)Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct BranchesSensors10.3390/s2104107421:4(1074)Online publication date: 4-Feb-2021
https://doi.org/10.3390/s21041074

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Research of automatic recognition of car license plates based on deep learning for convergence traffic control system

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning

Object detection using YOLO: challenges, architectural successors, datasets and applications

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Research of automatic recognition of car license plates based on deep learning for convergence traffic control system

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning

Object detection using YOLO: challenges, architectural successors, datasets and applications

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