Abstract:

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

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。

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