Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (7): 198-208.DOI: 10.3778/j.issn.1002-8331.2008-0137

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

MA Qiaomei, WANG Mingjun, LIANG Haoran   

  1. 1.School of Software, North China University, Taiyuan 030051, China
    2.Shanxi Military-Civilian Integration Software Technology Engineering Research Center, Taiyuan 030051, China
  • Online:2021-04-01 Published:2021-04-02



  1. 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


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.

Key words: target detection, YOLOv3, complex scenario, license plate location, CIoU, Inception-SE structure


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。

关键词: 目标检测, YOLOv3, 复杂场景, 车牌定位, CIoU, Inception-SE结构