计算机多尺度辅助定位车牌算法
License Plate Detection Algorithm Based on Computer Multi Scale Assist
查看参考文献19篇
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文摘
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违法占道拍摄出的单帧车辆图像具有数据量大、时效性强,检测环境复杂等特点.对其检测需要花费大量的人力与物力.并且人们在定位过程中,无法避免因经验、疲劳等方面的干扰,导致遗漏和错误定位.为此本文从视觉感知角度提出计算机多尺度辅助定位车牌算法.模拟视觉感知原理,从车辆特征、纹理特征、颜色特征尺度,逐次聚焦至车牌所在区域.提出了完整的单帧图像车牌定位流程.并且提出基于边界对的车牌区域准确定位算法.通过对实拍的交通图像实验,表明本算法对于正对的车辆有较高的准确率,符合人类视觉感知的过程可实时的对图像进行车牌检测,可同时检测单幅图片的多个车牌.但对于光线过暗、过强或者颜色失真的情况,仍需要进一步的研究. |
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其他语种文摘
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There are the characters of large data,strong effectiveness and complex environment in a single-frame image of illegal encroaching vehicles,the detection of illegal encroaching vehicles requires amount of experimental work and labor.In the process of positioning license plates,people cannot avoid some interference such as experience,fatigue,which results in missing and misplacing license plates.The computer-assisted location algorithm of license plates is proposed from the perspective of visual perception,and used the method of multiple scales in this paper.Simulating the visual perception principle,the computer successively focused to the license plate area according to vehicle characteristics,texture features,and color characteristics scale.We put forward a complete license plate positioning process for the single-frame image and propose an accurate location algorithm for license plate area based on boundary pair.The experiments on real traffic images show that the proposed algorithm has a higher accuracy rate when the image is acquired from the head or rear of vehicles,and is consistent with the process of human visual perception,and can detect multiple license plates on the single image at the same time.But further study need to be carried in the situation of too dark environment,too bright light or color distortion. |
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来源
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电子学报
,2018,46(9):2188-2193 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2018.09.020
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关键词
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计算机辅助定位
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视觉感知
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车牌
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多尺度
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边缘对
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地址
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1.
西安工业大学, 陕西, 西安, 710021
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中国科学院西安光学精密机械研究所, 陕西, 西安, 710119
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中国科学院深海科学与工程研究所, 海南, 三亚, 572000
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西安市公安局, 陕西, 西安, 710002
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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中国科学院西部之光人才培养计划
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中国科学院光谱成像重点实验室开放基金项目
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文献收藏号
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CSCD:6344783
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参考文献 共
19
共1页
|
|
1.
Panchal T. License plate detection using harris corner and character segmentation by integrated approach from an image.
Procedia Computer Science,2016,79:419-425
|
被引
2
次
|
|
|
|
|
2.
Tabrizi S S. A hybrid KNN-SVM model for iranian license plate recognition.
Procedia Computer Science,2016,102:588-594
|
被引
3
次
|
|
|
|
|
3.
Gou C. Vehicle license plate recognition based on extremal regions and restricted boltzmann machines.
IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1096-1107
|
被引
13
次
|
|
|
|
|
4.
费继友. 基于字符特征约束的自适应车牌校正提取.
仪器仪表学报,2016,37(3):632-639
|
被引
7
次
|
|
|
|
|
5.
Kim T. A vehicle license plate recognition system using morphological ROI(Region of Interest) map generated from morphology operation.
Journal of Physics:Conference Series,2017,806(1):012004
|
被引
1
次
|
|
|
|
|
6.
Makaoui K. Improved license plate localization.
Electrical and Information Technologies(ICEIT),2016 International Conference on,2016:402-405
|
被引
1
次
|
|
|
|
|
7.
Tian Y. An algorithm combined with color differential models for license-plate location.
Neurocomputing,2016,212:22-35
|
被引
2
次
|
|
|
|
|
8.
Rashedi E. A hierarchical algorithm for vehicle license plate localization.
Multimedia Tools and Applications,2018,77(2):2771-2790
|
被引
2
次
|
|
|
|
|
9.
Agarwal A. An efficient algorithm for automatic car plate detection & recognition.
Computational Intelligence & Communication Technology(CICT),2016 Second International Conference on,2016:644-648
|
被引
1
次
|
|
|
|
|
10.
陈庄. 多尺度积角点检测和视觉颜色特征的鲁棒车牌定位算法.
重庆大学学报:自然科学版,2016,39(2):89-98
|
被引
1
次
|
|
|
|
|
11.
杨硕. 多特征融合的车牌定位算法.
计算机应用,2016,36(6):1730-1734
|
被引
2
次
|
|
|
|
|
12.
Miyata S. Automated license plate detection using a support vector machine.
Control,Automation,Robotics and Vision(ICARCV),2016 14th International Conference on,2016:1-5
|
被引
1
次
|
|
|
|
|
13.
Jo J H. An ensemble classifier based method to select optimal image features for license plate recognition.
The Transactions of The Korean Institute of Electrical Engineers,2016,65(1):142-149
|
被引
1
次
|
|
|
|
|
14.
Liu Y. Convolutional neural networks-based intelligent recognition of Chinese license plates.
Soft Computing,2018,22(7):2403-2419
|
被引
2
次
|
|
|
|
|
15.
Friis J K B O. Gestalt descriptions embodiments and medical image interpretation.
Ai & Society,2017,32(2):209-218
|
被引
1
次
|
|
|
|
|
16.
邱实. 一种新策略的肺结节检测算法.
电子学报,2016,44(6):1413-1419
|
被引
3
次
|
|
|
|
|
17.
钱夔. 一种改进型机器人仿生认知神经网络.
电子学报,2015,43(6):1084-1089
|
被引
2
次
|
|
|
|
|
18.
曾接贤. 结合单双行人DPM模型的交通场景行人检测.
电子学报,2016,44(11):2668-2675
|
被引
10
次
|
|
|
|
|
19.
曹明伟. 基于多层背景模型的运动目标检测.
电子学报,2016,44(9):2126-2133
|
被引
2
次
|
|
|
|
|
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