基于改进图像增强及CNN的复杂环境车牌识别算法
Complex Environment License Plate Recognition Algorithm Based on Improved Image Enhancement and CNN
杨秀璋 1武帅 1任天舒 1廖文婧 1项美玉 2于小民 3刘建义 1陈登建1
作者信息
- 1. 贵州财经大学信息学院 贵阳 550025
- 2. 贵州财经大学大数据应用与经济学院(贵阳大数据金融学院) 贵阳 550025
- 3. 贵州财经大学贵州省经济系统仿真重点实验室 贵阳 550025
- 折叠
摘要
传统图像识别和深度学习模型较难检测复杂环境下的车牌,其场景适用性和精确率较低,从而严重威胁交通安全,影响智慧交通发展.基于此,文中提出了一种基于改进图像增强及CNN的复杂环境车牌识别算法.首先,结合目标图像的平均灰度值,利用ACE算法和暗通道先验去雾算法对复杂环境下的车牌数据集进行图像增强;然后提出了一种融合色彩关键特征和波峰关键特征的车牌区域定位算法,通过8个核心步骤有效定位复杂环境下车牌的区域;最后构建五层卷积神经网络的模型,以实现车牌字符识别.实验结果表明,所提算法能有效识别复杂环境下行驶车辆的车牌,该算法在复杂环境车牌区域定位的精确率为86.04％,召回率为82.60％,F1值为84.29％,其F1值比传统图像处理算法提升了47.29％,比SSD算法提升了24.73％,比YOLO算法提升了26.37％,比YOLOv3提升了17.15％.同时,所提方法的时间复杂度较低,属于一种轻量级的车牌识别方法,能消除噪声并实现车牌字符识别,具有一定的应用前景和实用价值,也将为智慧交通的研究提供理论基础.
Abstract
Traditional image recognition and deep learning models are difficult to detect license plates in complex environments.Their scene applicability and accuracy are low,which seriously threatens traffic safety and affects the development of intelligent transportation.This paper proposes a complex environment license plate recognition algorithm based on improved image enhance-ment and CNN.First,after calculating the average gray value of the target image,we use the ACE algorithm and the dark channel prior dehazing algorithm to perform image enhancement on the license plate dataset in complex environments.Then,a license plate area localization algorithm that combines the key features of color and the peak is proposed,effectively locating the license plate area by eight-core steps in a complex environment.Finally,a five-layer convolutional neural network model is constructed to recognize the license plate character.Experimental results show that the proposed algorithm can effectively identify the license plates of vehicles in complex environments.The precision of the algorithm's license plate area location in complex environments is 86.04％,the recall is 82.60％,and the F1-score is 84.29％.Among them,the F1-score of the proposed algorithm is 47.29％higher than the traditional image processing algorithm,24.73％higher than the SSD algorithm,26.37％higher than the YOLO algorithm and 17.15％higher than the YOLOv3 algorithm.At the same time,the time complexity of the proposed method is low,and it belongs to a lightweight license plate recognition method.Also,it can eliminate noise and realize license plate character re-cognition.Therefore,it has specific application prospects and practical value and provides a theoretical basis for intelligent trans-portation research.
关键词
车牌识别/图像增强/深度学习/复杂环境/智慧交通Key words
License plate recognition/Image enhancement/Deep learning/Complex environment/Intelligent transportation引用本文复制引用
基金项目
贵州省科技计划(黔科合基础[2020]1Y279)
贵州财经大学校级项目(2021)(2021KYQN03)
出版年
2024
NETL
NSTL
万方数据