复杂场景下基于改进YOLOv3的车牌定位检测算法  

License Plate Location Detection Algorithm Based on Improved YOLOv3 in Complex Scenes

作　　者：马巧梅[1,2] 王明俊 梁昊然 MA Qiaomei;WANG Mingjun;LIANG Haoran(School of Software,North China University,Taiyuan 030051,China;Shanxi Military-Civilian Integration Software Technology Engineering Research Center,Taiyuan 030051,China)

机构地区：[1]中北大学软件学院,太原030051 [2]山西省军民融合软件技术工程研究中心,太原030051

出　　处：《计算机工程与应用》2021年第7期198-208,共11页Computer Engineering and Applications

基　　金：山西省自然科学基金(201801D121026);山西省研究生创新项目(2020SY398)。

摘　　要：针对在光照、多车辆和低分辨率等复杂场景下车牌定位困难、检测速度慢和精度低等问题,提出了一种改进YOLOv3的方法。采用K-means++方法对实例的标签信息进行聚类分析获取新的anchor尺寸,通过改进后的精简特征提取网络(DarkNet41)来提高模型的检测效率并降低计算消耗。此外,改进了多尺度特征融合,由3尺度预测增加至4尺度预测并在检测网络中加入了改进后的Inception-SE结构来提高检测的精度,选取了CIoU作为损失函数。预处理方面用MSR(Multi-Scale Retinex)算法对数据进行增强。实验分析表明,采用该算法mAP(均值平均精度)达到了98.84%,检测速度达到36.4帧/s,与YOLOv3模型以及其他算法相比具有更好的准确性和实时性。Aiming at the problem of the difficulty of license plate positioning,slow detection speed and low detection accuracy in complex scenes such as lighting,multi-vehicle and low resolution,an improved method based on YOLOv3 is proposed.Firstly,the label information of the example is clustered by K-means++method to obtain a new anchor size.And then,the improved thin feature extraction network(DarkNet41)is used to improve the detection efficiency of the model and reduce computational consumption.Moreover,multi-scale feature fusion is improved from 3-scale prediction to 4-scale prediction and improved Inception-SE structure is added to the detection network to improve the accuracy of detection.Finally,CIoU is selected as a loss function.The data is enhanced with the Multi-Scale Retinex(MSR)algorithm.Experimental analysis shows that the improved algorithm’s mAP reaches 98.84%and the detection speed reaches 36.4 frame/s,which has better accuracy and real-time performance compared with the YOLOv3 model and other algorithms.

关 键 词：目标检测 YOLOv3 复杂场景 车牌定位 CIoU Inception-SE结构 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]