Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach

Wang, Yi; Bian, Zhen-Peng; Zhou, Yunhao; Chau, Lap-Pui

doi:10.1109/tits.2021.3087158

Cited by 40 publications

(16 citation statements)

References 42 publications

(93 reference statements)

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“…The license plate number in the video can be processed at a speed of 9 frames per second. Wang et al [10] proposed a novel two-stage detection method. Their proposed method uses VertexNet and SCR-Net to combine, their detection uses VertexNet method, and their recognition uses SCR-Net.…”

Section: Related Workmentioning

confidence: 99%

The Low-light License Plate Recognition via CNN

Zheng¹,

Guan²,

Li³

2023

J. Phys.: Conf. Ser.

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The low-light license plate recognition (LPR) is an important task in LPR, and the task of low-light LPR is a challenge in LPR. Compared with ordinary LPR, low-light LPR is more challenging. The first is that there are few studies on low-light LPR, and there is a lack of dedicated datasets. Besides, there are few lightweight networks dedicated to low-light LPR. The lack of lightweight private networks makes it difficult to deploy LPR methods efficiently. Based on this, this paper proposes a low-light LPR method. Specifically, we propose a dataset dedicated to low-light LPR with a sample size of 19,121. Besides, we propose a lightweight LPR method implemented using a lightweight architecture similar to VGG. We also compare with some commonly used lightweight methods, and the comparison results show that our proposed method has better performance. In addition, we also visualized some data that are difficult for the model to identify, and found several types of challenging data. This result is helpful for subsequent researchers to improve on this type of data.

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Section: Related Workmentioning

confidence: 99%

The Low-light License Plate Recognition via CNN

Zheng¹,

Guan²,

Li³

2023

J. Phys.: Conf. Ser.

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“…Wang et al [3] propose a two-step process for license plate detection and recognition by designing two specialized networks. For detection, VertexNet is designed to extract the spatial information of the license plate and later on uses this information to rectify the detected license plate image.…”

Section: B License Plate Detection and Recognitionmentioning

confidence: 99%

“…Leveraging upon the installed surveillance cameras of toll plazas, we propose an image-based solution for toll tax collection. Such a solution is motivated by the recent state-ofthe-art results achieved by the usage of convolution neural network (CNN) for image-based vehicle type [2] and license plate recognition [3]. Our framework calculates the toll tax of a vehicle from its image using three steps strategy.…”

Section: Introductionmentioning

confidence: 99%

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

Usama¹,

Anwar²,

Shahid³

et al. 2022

Preprint

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We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image variations caused by several factors. The traditional vehicle decorations on the front cause variations among vehicles of the same type. These decorations make license plate localization and recognition difficult due to severe background clutter and partial occlusions. Likewise, on most vehicles, specifically trucks, the position of the license plate is not consistent. Lastly, for license plate reading, the variations are induced by non-uniform font styles, sizes, and partially occluded letters and numbers. Our proposed framework takes advantage of both data availability and performance evaluation of the backbone deep learning architectures. We gather a novel dataset, Diverse Vehicle and License Plates Dataset (DVLPD), consisting of 10k images belonging to six vehicle types. Each image is then manually annotated for vehicle type, license plate, and its characters and digits. For each of the three tasks, we evaluate You Only Look Once (YOLO)v2, YOLOv3, YOLOv4, and FasterRCNN. For real-time implementation on a Raspberry Pi, we evaluate the lighter versions of YOLO named Tiny YOLOv3 and Tiny YOLOv4. The best Mean Average Precision

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“…Wang et al [16] was presenteda weight-sharing classifier that can detect all occurrences of a character in any place. Small cross-dataset studies have been conducted in recent years to test the generalizability of the suggested approaches, since recognition rates using the classic split methodology have grown dramatically in recent years.…”

Section: Introductionmentioning

confidence: 99%

Character level vehicle license detection using multi layered feed forward back propagation neural network

Ummadisetti

Thiruvengatanadhan²,

Narayana³

et al. 2023

Bulletin EEI

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Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

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Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach

Cited by 40 publications

References 42 publications

The Low-light License Plate Recognition via CNN

The Low-light License Plate Recognition via CNN

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

Character level vehicle license detection using multi layered feed forward back propagation neural network

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