基于语义对齐和层次优化的非机动车车牌识别定位方法

Non-Motorized License Plate Recognition and Localization Method Based on Semantic Alignment and Hierarchical Optimization

对非机动车违规行为依法追究责任是提高城市交通安全的有效手段。由于非机动车车牌具有尺寸小、分布密集、易遮挡等特点,导致应用传统的深度学习方法会出现特征信息大量丢失的现象。为此,提出一种基于语义对齐和层次优化的非机动车车牌识别定位方法。首先设计底层信息融合的语义对齐模块,在上采样过程中利用底层目标信息引导高层语义向下融合,以解决高底层语义冲突带来的小目标特征丢失问题;然后构建CSP结构的层次优化模块替代深层ELAN模块,使用堆叠少量卷积核模块提取目标信息以减少网络层数,避免特征信息在深层丢失;最后,为减少训练过程中的匹配误差,使用K-Means++算法聚类得到适合非机动车车牌的初始锚框,提高小目标识别定位准确率。实验结果表明,所提方法在自制非机动车车牌数据集上的识别定位准确率为90.95%,与YOLOv7、YOLOv8等代表性方法相比至少提升3.58%,为非机动车车牌识别定位提供了一种有效的方法。

Holding non-motorized vehicles accountable for legal violations effectively enhances urban traffic safety.Non-motorized vehicle license plates are characterized by small size,dense distribution,and ease of being obscured,which leads to significant feature information loss during the detection process in traditional deep learning-based methods.A non-motorized vehicle license plate recognition and localization method based on semantic alignment and hierarchical optimization is proposed.In this method,a semantic alignment module is designed for the underlying information fusion.During the upsampling process,low-level target information is used to guide the fusion of high-level semantics downwards,addressing the loss of small target features caused by conflicts between high-and low-level semantics.Subsequently,a hierarchical optimization module is constructed within the CSP structure to replace the deep ELAN module.This module uses a stack of a few convolutional kernel modules to extract the target information,reducing the number of network layers and preventing the loss of feature information at deeper levels.In the final stage,the K-Means++algorithm is employed to cluster and obtain the initial anchor boxes suitable for non-motorized license plates to reduce the matching error during the training process.This approach aims to improve the accuracy of small-object recognition and localization.The experimental results demonstrate that the proposed method achieves a recognition and localization accuracy of 90.95%on a non-motorized vehicle license plate dataset.Compared with representative methods such as YOLOv7 and YOLOv8,it improves the accuracy by at least 3.58%.The proposed approach is effective for non-motorized vehicle license plate recognition and localization.