Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation
arXiv:2606.05785v1 Announce Type: cross Abstract: 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
The continuous drive for efficiency and accuracy in real-time AI applications, particularly in smart city infrastructure, necessitates ongoing algorithmic improvements.
This research outlines a significant advancement in real-time LPDR systems, enhancing their accuracy and robustness for critical applications in urban surveillance and management.
The introduction of Cross-Spatial Hybrid Attention and Class-Balanced Synthetic Augmentation improves LPDR model performance, addressing inaccuracies caused by character mismatches and data imbalance.
- · Smart city technology providers
- · Urban planners
- · Law enforcement
- · AI/ML researchers
- · Legacy LPDR system manufacturers
Improved accuracy in license plate recognition leads to more efficient traffic management and enhanced security capabilities.
The reduced error rates could enable broader adoption of automated tolling systems and vehicle tracking in various urban scenarios.
Increased reliance on such systems might raise public discourse around privacy implications and data governance in smart cities.
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Read at arXiv cs.LG