提升车牌识别速度与小众省份车牌识别率
Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation
- 引入跨空间注意力与类别平衡增广，优化并行解码架构
- 少数省份车牌识别率从78.2%提升至91.5%，保持152帧/秒
- 适合智能交通、实时车牌识别系统研发者参考
实时车牌检测与识别（LPDR）是智慧城市建设的核心。尽管YOLOV5-PDLPR模型通过并行解码显著提升了系统效率，但其性能仍受空间字符错位和训练数据不平衡的影响。本文提出交叉空间混合注意力（CSHA）与类别平衡合成增广（CBSA），基于75,000张合成样本，在CCPD、CLPD、PKU及一个特定应用数据集上进行评估。实验表明，少数省份车牌识别率从78.2%提升至91.5%，同时保持152 FPS的实时处理性能。结果表明，具备空间感知的并行解码结合类别平衡增广，为高速车牌识别系统提供了有效解决方案。
原文摘要 · Abstract (English)
Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improved system efficiency through a parallel decoder approach, its performance is still affected by spatial character mismatches and data imbalance within the training set. This paper addresses these limitations by introducing Cross-Spatial Hybrid Attention (CSHA) and Class-Balanced Synthetic Augmentation (CBSA). An extensive study involving 75,000 synthetic samples is conducted and evaluated on four benchmarks: CCPD, CLPD, PKU, and an application-specific dataset. Experimental results demonstrate a substantial improvement in the recognition rate of minority provincial license plates from 78.2% to 91.5% while maintaining real-time processing performance of 152 FPS. The results indicate that spatially-aware parallel decoding combined with class-balanced augmentation provides an effective solution for high-speed license plate recognition systems.
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