Non-Segmented Chinese License Plate Recognition Algorithm based on Deep neural Networks

Gong, Wenbin; Shi, Zhangsong; Ji, Qiang

doi:10.1109/ccdc49329.2020.9163986

Cited by 6 publications

(7 citation statements)

References 22 publications

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“…Convolutional Neural Networks is used in [ 78 ] in real time scenario and has shown great results for each stage of ANPR system. Neural network based algorithms seems promising for ANPR and are proposed in [ 19 , 103 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 ].…”

Section: Discussionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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Section: Discussionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…In LSTM networks, memory cells are designed to maintain their state over time and learn long-term dependencies. RNNs have been used for license plate recognition [ 85 ], lane line detection [ 63 ], and crack classification [ 76 ] tasks, as well as in autonomous vehicle applications [ 86 ].…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…Hybrid methods include a combination of multiple ML or DL methods used in CV techniques. There are many intelligent transportation applications for this approach, such as license plate recognition [ 85 , 100 , 101 ], video anomaly detection [ 68 , 89 , 92 , 102 ], automatic license plate recognition [ 25 , 103 ], vehicle detection [ 11 , 12 , 53 , 55 ], pedestrian detection [ 58 , 104 ], lane line detection [ 63 , 105 ], obstacle detection [ 106 , 107 , 108 , 109 , 110 ], structural damage detection [ 111 , 112 , 113 ], and autonomous vehicle applications [ 13 , 114 , 115 ].…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…Transformers are used for image classification, object detection, and image compression in CV applications. In the field of ITS, they have been used in license plate recognition [ 85 ], pedestrian detection [ 117 ], and driver distraction detection [ 118 ] studies.…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…In [ 40 ], a CNN was used in a real-time scenario, achieving good results for each phase of the ANPR system. Neural-network-based methods seem to be promising solutions for ANPR systems, and have been utilized in a number of studies, including [ 21 , 85 , 129 , 130 , 131 , 132 , 133 ].…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

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Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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