Research on License Plate Location Algorithm Based on YOLOv5

Zhu, Qiaoming; Liu, Yingnan; Zhao, Zihao; Xin, Ma

doi:10.1088/1742-6596/2278/1/012040

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The number of parameters of +GAM is 4.02 M, and the number of parameters of SYOLOv5 is 3.22 M. In contrast, the number of parameters of the model of the proposed algorithm in this paper is 5.07 M, which is 28.2% less than that of the original model. In order to further verify the effectiveness of this paper's algorithm, this paper and the classic lightweight model of license plate recognition for performance comparison, the literature [4][5][6][7][8] is a lightweight network for the improvement of Shuf-fleNetv2, in order to ensure that the comparison algorithm has a unified evaluation standard [9][10][11][12][13][14][15][16][17][18][19], to ensure the performance of the network processing [20][21][22][23][24][25][26][27][28], we will be the above algorithms in the perfor mance comparison on the CCPD dataset, and the results of the comparison are shown in Table 5. Through data comparison we can find that the improved algorithms in this paper have the highest accuracy rate, the smallest number of parameters, and the shortest running time.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A lightweight license plate detection algorithm based on deep learning

Zhu,

Wang,

Wang

2023

IET Image Processing

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License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A lightweight license plate detection algorithm based on deep learning

Zhu,

Wang,

Wang

2023

IET Image Processing

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“…The license plate has no fixed location because it might appear anywhere in the video frame. Several factors influence license plate localization, including illumination, image quality, and other factors [7]. The flowchart for the license plate localization stage is shown in Figure 2 below.…”

Section: License Plate Localizationmentioning

confidence: 99%

A Robust License Plate Detection System Using Smart Device

Bin Mohamad Azhar,

Goh,

Check Yee

et al. 2024

JOIV : Int. J. Inform. Visualization

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The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

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“…YOLOv5m network will adaptively calculate the corresponding a priori anchor box values according to different data sets, and its initial a priori anchor boxes are (10,13), (16,30), (33,23), (30,61), (62,45), (59,119), (116,90), (156,198) and (373,326). The prior anchor boxes obtained by K-means++ algorithm clustering are (12,16), (17,39), (30,52), (54,60), (33,26), (126,183), (227,283), (373,326) and (407,486) respectively.…”

Section: B Improvement Of Yolov5m Algorithm 1) Improve the Matching D...mentioning

confidence: 99%

“…Vigita et al [9] introduced a novel method called partial character reconstruction to segment license plate characters, developed an automatic license plate recognition system that can cope with many factors, and enhanced the performance of the license plate recognition system. In order to solve the problem of license plate location in the low resolution multi vehicle environment, Zhu et al [10] proposed a deep learning license plate recognition method based on improved YOLOv5s. The algorithm has good robustness and fast operation speed.…”

Section: Introductionmentioning

confidence: 99%

Research on Car License Plate Recognition Based on Improved YOLOv5m and LPRNet

Luo

Liu

2022

IEEE Access

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The application of license plate recognition technology is becoming more and more extensive. In view of the current practical requirements for the recognition accuracy and real-time performance of license plate recognition system in complex scenes, the existing target detection methods and license plate recognition methods are studied, and a car license plate recognition method based on improved YOLOv5m and LPRNet model is proposed. On the basis of studying the YOLOv5m algorithm and the image features of the car license plate, the YOLOv5m algorithm is improved from three aspects: the K-means++ algorithm is used to improve the matching degree between the anchor frame and the detection target, the DIOU loss function is used to improve the NMS method, and the feature map with 20× 20 is removed to reduce the number of detection layers. A lightweight LPRNet network is used to realize license plate character recognition without character segmentation. Combining the improved YOLOv5m algorithm with LPRNet network, a license plate recognition system based on IYOLOv5m-LPRNet model is designed. The experimental results show that the average recognition accuracy of license plates in front, tilt, night and strong light interference scenes is more than 98%; Compared with the models of YOLOv3-LPRNet, YOLOv4-LPRNet, YOLOv5s-LPRNet and YOLOv5m-LPRNet, the recognition accuracy and recall rate of this method are improved, reaching 99.49% and 98.79% respectively; The mAP of this method is also the highest, reaching 98.56%; In terms of recognition speed, this method is also faster than the other four methods, and the number of pictures processed per second is increased by 5 compared with the YOLOv5m-LPRNet model. Therefore, the improved license plate recognition method in this paper performs well in robustness and speed.

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Research on License Plate Location Algorithm Based on YOLOv5

Cited by 6 publications

References 10 publications

A lightweight license plate detection algorithm based on deep learning

A lightweight license plate detection algorithm based on deep learning

A Robust License Plate Detection System Using Smart Device

Research on Car License Plate Recognition Based on Improved YOLOv5m and LPRNet

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