基于级联CNNs的非约束车牌精确定位

为解决单一深层卷积神经网络用于非约束场景下车牌定位时，所输出的矩形检测框对非正面车牌定位效果不佳的问题，提出将目标检测与目标分类CNN网络级联，通过检测网络得到感兴趣区域，接着利用轻量级分类网络，将车牌顶点检测问题转化为回归问题。首先，利用YOLOv3网络进行粗定位，获取图像中所有车牌的候选区域；然后，使用基于MobileNetV3改进的轻量级神经网络定位候选区域中的车牌顶点，实现车牌区域精定位；最后，通过透视变换将车牌区域投影到矩形框内实现车牌校正。实验结果表明，所提出的级联CNNs能够有效解决单一CNN目标检测网络仅能输出矩形检测框，而不适用于非约束车牌定位的问题，具有较好的应用价值。

Accurate location of unconstrainedlicense plate based on cascaded CNNs

To solve the problem that the rectangular detection box outputted from a single deep convolutional neural network (CNN) cannot deal with the non-front license plate location well under unconstrained scenarios, a solution based on cascaded CNNs is proposed. The solution cascades the target detection and target classification CNN network, uses the detection network to obtain the region of interest, and then uses the lightweight classification network to transform the license plate vertex detection problem into a regression problem. Firstly, the YOLOv3 network is used for locating license plates roughly to obtain all the license plate candidate areas in an image. Then, an improved MobileNetV3 lightweight CNN is adopted to locate the license plate vertices in the candidate areas to achieve the precise location of license plate areas. Finally, the license plate is corrected and projected into the rectangular box through perspective transformation. Experiment results show that the proposed cascaded CNNs can effectively solve the problem that a single CNN object detection network can only output a rectangular detection box and is not suitable for unconstrained license plate detection. It has a good application potential.