Tao Lingbing, Hong Shunhe, Lin Yongxing, Chen Yangbing, He Pingan, Tie Zhixin
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Keyi College, Zhejiang Sci-Tech University, Shaoxing 312369, China.
Sensors (Basel). 2024 Apr 27;24(9):2791. doi: 10.3390/s24092791.
Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes.
从自然场景图像中准确快速地识别车牌是一项至关重要且具有挑战性的任务。现有方法能够在简单场景中识别车牌，但在复杂环境下其性能会显著下降。本文提出了一种新颖的车牌检测与识别模型YOLOv5-PDLPR，该模型在车牌检测部分采用YOLOv5目标检测算法，在车牌识别部分使用本文提出的PDLPR算法。PDLPR算法主要设计如下：（1）采用多头注意力机制精确识别单个字符。（2）设计全局特征提取器网络以提高网络特征提取的完整性。（3）采用最新的并行解码器架构提高推理效率。实验结果表明，所提算法比对比算法具有更好的准确性和速度，能够实现实时识别，并且在复杂场景中具有较高的效率和鲁棒性。
