复杂场景下基于改进YOLOv3的车牌定位检测算法

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。

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

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.