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240620s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3414269
|2 doi
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Ding, Haoxuan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a FF-LPD
|b A Real-Time Frame-by-Frame License Plate Detector With Knowledge Distillation and Feature Propagation
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 26.06.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a With the increasing availability of cameras in vehicles, obtaining license plate (LP) information via on-board cameras has become feasible in traffic scenarios. LPs play a pivotal role in vehicle identification, making automatic LP detection (ALPD) a crucial area within traffic analysis. Recent advancements in deep learning have spurred a surge of studies in ALPD. However, the computational limitations of on-board devices hinder the performance of real-time ALPD systems for moving vehicles. Therefore, we propose a real-time frame-by-frame LP detector focusing on real-time accurate LP detection. Specifically, video frames are categorized into keyframes and non-keyframes. Keyframes are processed by a deeper network (high-level stream), while non-keyframes are handled by a lightweight network (low-level stream), significantly enhancing efficiency. To achieve accurate detection, we design a knowledge distillation strategy to boost the performance of low-level stream and a feature propagation method to introduce the temporal clues in video LP detection. Our contributions are: (1) A real-time frame-by-frame LP detector for video LP detection is proposed, achieving a competitive performance with popular one-stage LP detectors. (2) A simple feature-based knowledge distillation strategy is introduced to improve the low-level stream performance. (3) A spatial-temporal attention feature propagation method is designed to refine the features from non-keyframes guided by the memory features from keyframes, leveraging the inherent temporal correlation in videos. The ablation studies show the effectiveness of knowledge distillation strategy and feature propagation method
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Gao, Junyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuan, Yuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Qi
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 06., Seite 3893-3906
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:33
|g year:2024
|g day:06
|g pages:3893-3906
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|u http://dx.doi.org/10.1109/TIP.2024.3414269
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