摘要
针对雾霾天气下车牌识别存在的精确度低、漏检等问题,提出了一种基于深度学习的雾霾天气下的车牌号码识别方法。首先用AOD-Net算法对车辆图像进行去雾预处理。然后,基于YOLOv5网络设计一种车牌检测网络ACG_YOLOv5s。ACG_YOLOv5s是在YOLOv5s网络的基础上,融入CBAM注意力机制,提高模型的抗干扰能力;引入自适应特征融合网络ASFF,根据模型自适应学习到的权重赋予网络不同特征层不同比重的权值,从而突出重要特征信息;使用Ghost卷积模块替换传统卷积,在保证模型效果的同时减少了网络训练过程中的参数量。最后通过LPRNet对检测到的车牌图像进行识别。实验结果表明,改进后的ACG_YOLOv5s网络车牌检测准确率达到99.6%,LPRNet识别准确率达96%且内存占比小。实验证明AOD-Net算法和YOLO算法结合可更加有效地检测雾霾天气下车牌图像中的车牌号码。
A deep learning based license plate number recognition method is proposed to address the issues of low accuracy and missed detection in license plate recognition under haze weather.Firstly,the AODNet algorithm is used to pre-process the vehicle image for defogging.Then,a license plate detection network ACG_YOLOv5s is designed based on YOLOv5 network.ACG_YOLOv5s integrates CBAM attention mechanism on the basis of YOLOv5s network to improve the model’s anti-interference ability.An adaptive feature fusion network(ASFF)is introduced,which assigns weights to different feature layers of the network based on the weights adaptively learned by the model,thereby highlighting important feature information.The traditional convolution is replaced with Ghost convolution module and the number of parameters during network training is reduced while ensuring model performance.Finally,LPRNet is used to recognize the detected license plate images.The experimental results indicate that the improved ACG_YOLOv5s network has a license plate detection accuracy of 99.6%,LPRNet recognition accuracy of 96%,and a small memory footprint.The combination of AOD-Net algorithm and YOLO algorithm can more effectively detect license plate numbers in license plate images under haze weather.
作者
杨云
王静
姜佳乐
YANG Yun;WANG Jing;JIANG Jiale(School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi'an 710021,China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2024年第2期205-216,共12页
Chinese Journal of Liquid Crystals and Displays
基金
国家自然科学基金(No.61971272,No.61601271)
国家重点研发重点专项(No.2019YFC1520204)。
