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.

https://www.mdpi.com/1424-8220/21/9/3028/pdf

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms.

Due to the need to detect and track stolen and criminal vehicles and traffic monitoring, the development of license plate recognition systems on intersection monitors system is very urgent for the development of smart cities. Unlike the traditional license plate recognition technology applied to smart parking lots to identify a single license plate in a single lane, license plate recognition applied to intersection monitors must detect multiple license plates on multiple lanes. In addition, license plate recognition applied to intersection monitors faces many challenges, including too small license plates in the picture, unstable light sources, different shooting angles, blurred license plate characters in moving vehicles, and complex road conditions, advertising signs, traffic signs and road name indicator. To solve the above problems, this paper proposes a new two-stage methodology based on deep learning technology which first detects all the license plates in a picture and extracts the license plate images, and then performs character recognition on the license plate images using Convolutional Neural Networks. Through the two-stage approach, this method increases the proportion of characters in the picture, which in turn improves the character recognition accuracy. Experimental results show that the methodology achieves 98.23% of license plate detection rate and 97.38% of character recognition rate. The performance of the hierarchical methodology is about 25 fps. This methodology shows the superiority in both accuracy and performance in comparison with traditional license plate recognition systems.

Two-Stage License Plate Recognition System using Deep learning

Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimised train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task. 

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License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.

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Study for License Plate Detection

License plate location is the key step of license plate recognition. In order to improve the precision of license plate location, a license plate location method based on deep learning and feature fusion is proposed in this paper. Firstly, the color feature of license plate is extracted by histogram backprojection method, and the texture feature of license plate is extracted by Sobel Operator. Combining these two kinds of features, the obtained connectivity regions are used as the candidate regions of license plate. And then ResNet20 convolutional neural network is used to identify the candidate regions and locate the license plate. The test results show that the method can satisfy the precise license plate location under the different shooting angles and illuminations, and lay a foundation for improving the accuracy and robustness of license plate recognition in the future.

License Plate Location Based on Combination of Deep Learning and Feature Fusion

Vehicle license plate, also known as a number plate, represents a legal license to participate in the public traffic. It plays an important role in detecting stolen vehicles, controlling traffic volume, ticketing speeding vehicles, and so on. In this paper, we presented a deep learning method for the detection of car license plate. We train a region proposal network and use the output of the RPN to train the R-CNN. The training time for complex large images is shortened to a controllable range. The detection time of the target area is shortened to quasi real time, and the accuracy is also considerable.

Ling-Ying Hou

Papers

Chinese License Plate Detection Based on Deep Neural Network