| dc.description.abstract | Edge-based Automatic License Plate Recognition ALPR Systems are having a hard
time serving the real-time detection accuracy in complex CCTVs. The state-of-the-art
high-resolution solvers rely on server-class hardware with over 50 GFLOPs and 200 MB
memory, which cost between USD 5000–8000 per node. On the other hand, lightweight
edge models provide low detection precision (<0.75) and recognition accuracy (40%)
rendering use limited in practice of traffic monitoring and law enforcement. In this
paper we introduce a real-time on-the-edge ALPR system that exceeds the 25 FPS
mark, running at commodity end-point devices with hardware computational budget of
less than 10 GFLOPs and memory footprint below 100 MB, while yielding detection
precision greater than 0.85 with recognition accuracy exceeding 0.90. The adopted
method is composed of multi-scale knowledge distillation to transfer semantic and
spatial information from a high-capacity teacher network to a thin student model
through pixel-wise and attention-aware manners. Feature Pyramid Networks can
cope with variable license plate sizes (50–200 pixels) in real-world CCTV images.
Quantization-Aware Training, which effectively converts weights and activations to 8-bit
integer precision, reduces the computation cost by 0.75. The dataset augmentation
for the CCTV setting adds condition-dependent augmentations including motion blur
(speeds of vehicles up to 120 km/h) as well as illumination changes (10 to 100,000
lux), compression, and viewing angles between ±45°.This approach provides a cost effective, privacy-preserving on-device ALPR solution that can potentially lower the deployment costs by 10–50 times as compared to server-based systems and scale
intelligent transportation systems in resource-limited domains. | en_US |
