New approach to vehicle license plate location based on new model YOLO‐L and plate pre‐identification

Min, Weidong; Li, Xiangpeng; Wang, Qi; Qing-peng, Zeng; Liao, Yanqiu

doi:10.1049/iet-ipr.2018.6449

Cited by 31 publications

(14 citation statements)

References 26 publications

(34 reference statements)

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“…Min et al utilize YOLOv2 for detection. They use k-means++ clustering algorithm to select the best number and size of plate candidate boxes and based on this information they modify the structure and depth of YOLOv2 model [18]. Tao et al compared YOLO and SSD [33] in terms of LP detection and reported that YOLO achieved better accuracy [19].…”

Section: Related Workmentioning

confidence: 99%

“…YOLO has reported good precision and recall rates besides its fast execution times ((around 70 FPS) (76.8% mAP over the PASCAL-VOC dataset)). In many recent works, YOLO is used for real-time LP detection [15], [18]- [20]. In this work, a smaller version of YOLO, namely YOLOv3-tiny, is used to achieve even faster execution times while maintaining detection performance.…”

Section: B Lp Detectionmentioning

confidence: 99%

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Vehicle License Plate Detector in Compressed Domain

Beratoglu

Töreyi̇n

2021

IEEE Access

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Data compression techniques allow data size to be reduced prior to data transmission and involve decompression upon transfer. This study shows for the first time that license plate (LP) detection can be accomplished without full decompression of the encoded data. Therefore, by determining in advance which images are required for LP recognition, computational costs of the system can be reduced. The proposed approach is realized on High Efficiency Video Coding (HEVC) based compressed video sequences. Two methods are provided that generate images from HEVC attributes. Fully decoded pixel domain images are also generated for comparative purposes from the same encoded data. The YOLO V3 Tiny Object Detector is used in order to detect LPs in the generated images. EnglishLP, a public dataset, is used to interpret the findings in terms of speed and precision and for comparison with previous studies. An additional contribution of the paper is that a new compressed domain LP database has been created and made publicly available, comprising images captured by a commercial license plate recognition system. Using at least two-orders-of-magnitude less amount of data, the proposed compressed domain LP detector achieved similar precision and recall values to those of the state-of-the-art LP detection schemes tested on both datasets. Moreover, the proposed method results in more than 30% saving in inference time. The results suggest that the proposed method can be utilized for rapid video archive searching applications.

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Section: Related Workmentioning

confidence: 99%

Section: B Lp Detectionmentioning

confidence: 99%

Vehicle License Plate Detector in Compressed Domain

Beratoglu

Töreyi̇n

2021

IEEE Access

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“…In [49], an inventive vehicle license plate location system using the latest YOLO-L model and pre-identification plate was developed by Min. The proposed model modifies two parts to discover the area of the license plate precisely.…”

Section: Yolo (You Only Look Once)mentioning

confidence: 99%

Vehicle Number Plate Detection and Recognition Techniques: A Review

Parvin¹,

Rozario²,

Islam³

2021

Adv. sci. technol. eng. syst. j.

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Vehicle number plate detection and recognition is an integral part of the IntelligentTransport System (ITS) as every vehicle has a number plate as part of its identity. The quantity of vehicles on road is growing in the modern age, so numerous crimes are also increasing day by day. Almost every day the news of missing vehicles and accidents are perceived. Vehicles tracking is often required to investigate all these illegal activities. So, vehicle number plate identification, as well as recognition, is an active field of study. However, vehicle number plate identification has always been a challenging task for some reasons, for example, brightness changes, vehicle shadows, and non-uniform license plate character type, various styles, and environment color effects. In this review work, various state-of-the-art vehicle number plate detection, as well as recognition strategies, have been outlined on how researchers have experimented with these techniques, which methods have been developed or used, what datasets have been focused on, what kinds of characters have been recognized and how much progress have been achieved. Hopefully, for future research, this review would be very useful.

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“…This has made the model much faster than the two stage object detectors. Various applications on enhanced version of you only look once (YOLO) model have been proposed such as YOLO-L for vehicle license plate detection [28], YOLOv3-MobileNet for detection of electronic component [29], TF-YOLO for detection of multiple object from aerial images [30], YOLO-CA for car accident detection [31] and YOLO-UA for traffic flow monitoring [32]. These proposed techniques modified the network model to solve object detection problems in respective applications.…”

Section: Introductionmentioning

confidence: 99%

An optimized YOLO‐based object detection model for crop harvesting system

Junos

Khairuddin

Thannirmalai³

et al. 2021

IET Image Processing

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The adoption of automated crop harvesting system based on machine vision may improve productivity and optimize the operational cost. The scope of this study is to obtain visual information at the plantation which is crucial in developing an intelligent automated crop harvesting system. This paper aims to develop an automatic detection system with high accuracy performance, low computational cost and lightweight model. Considering the advantages of YOLOv3 tiny, an optimized YOLOv3 tiny network namely YOLO-P is proposed to detect and localize three objects at palm oil plantation which include fresh fruit bunch, grabber and palm tree under various environment conditions. The proposed YOLO-P model incorporated lightweight backbone based on densely connected neural network, multi-scale detection architecture and optimized anchor box size. The experimental results demonstrated that the proposed YOLO-P model achieved good mean average precision and F1 score of 98.68% and 0.97 respectively. Besides, the proposed model performed faster training process and generated lightweight model of 76 MB. The proposed model was also tested to identify fresh fruit bunch of various maturities with accuracy of 98.91%. The comprehensive experimental results show that the proposed YOLO-P model can effectively perform robust and accurate detection at the palm oil plantation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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New approach to vehicle license plate location based on new model YOLO‐L and plate pre‐identification

Cited by 31 publications

References 26 publications

Vehicle License Plate Detector in Compressed Domain

Vehicle License Plate Detector in Compressed Domain

Vehicle Number Plate Detection and Recognition Techniques: A Review

An optimized YOLO‐based object detection model for crop harvesting system

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