Aiming at the problems of license plate location difficulty and low character recognition accuracy in complex environments, such as a small number of license plate samples, illumination transformation, changeable weather and motion blur, this paper proposes an end-to-end license plate recognition method to improve the location and recognition accuracy in complex environments. First, the cyclic generative adversarial network is used to synthesize the approximate real license plate image to enrich the training set and solve the problem of data imbalance to facilitate subsequent model training. Second, a MF-RepUnet license plate location method is proposed, which integrates the improved VGG structure and feature pyramid into the U-Net model to improve the feature extraction capability of the network, and effectively solve the problem of missing detection of inclined license plate and small-scale license plate. Finally, the convolutional recurrent neural network is improved to accurately predict the feature sequence through the way of attention mechanism weighting, which solves the problem of blurred semantic structure sequence features caused by image degradation and further improves the accuracy of license plate character recognition. Experiments show that the proposed method can effectively improve the accuracy and efficiency of license plate location and character recognition, and can be applied to license plate recognition in various complex environments.

针对车牌样本少、光照变换、天气多变、运动模糊等复杂环境下车牌定位困难、字符识别准确率低的问题，提出一种端到端的车牌识别方法提高复杂环境下的定位和识别精度。首先，利用循环生成对抗网络合成近似真实车牌图像，丰富训练集，解决数据不平衡问题，方便后续模型训练。其次，提出了一种MF-RepUnet车牌定位方法，将改进后的VGG结构和特征金字塔融合到U-Net模型中，提高网络的特征提取能力，有效解决倾斜车牌和小规模车牌漏检问题。最后对卷积递归神经网络进行改进，通过注意力机制加权的方式准确预测特征序列，解决了图像退化导致的语义结构序列特征模糊问题，进一步提高了车牌字符识别的准确率。实验表明，该方法能有效提高车牌定位和字符识别的准确率和效率，可应用于各种复杂环境下的车牌识别。改进卷积循环神经网络，通过注意力机制加权的方式准确预测特征序列，解决了图像退化导致的语义结构序列特征模糊问题，进一步提高了车牌字符识别的准确率。实验表明，该方法能有效提高车牌定位和字符识别的准确率和效率，可应用于各种复杂环境下的车牌识别。改进卷积循环神经网络，通过注意力机制加权的方式准确预测特征序列，解决了图像退化导致的语义结构序列特征模糊问题，进一步提高了车牌字符识别的准确率。实验表明，该方法能有效提高车牌定位和字符识别的准确率和效率，可应用于各种复杂环境下的车牌识别。