Efficient real-time license plate recognition using deep learning on edge devices
<p dir="ltr">Real-time automatic license plate recognition (ALPR) is essential for smart-traffic, tolling, parking, and policing, yet roadside cameras must run on W hardware with limited memory and patchy connectivity, ruling out cloud off-loading. These constraints demand compact, f...
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2025
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| Summary: | <p dir="ltr">Real-time automatic license plate recognition (ALPR) is essential for smart-traffic, tolling, parking, and policing, yet roadside cameras must run on W hardware with limited memory and patchy connectivity, ruling out cloud off-loading. These constraints demand compact, fast models resilient to oblique views, motion blur, glare, and diverse plate styles. We introduce <i>Light-Edge</i>, a single-pass deep network that jointly localizes plates and recognizes characters. It shares a ResNet-18 + FPN backbone, removes 28 % of convolutions with a channel-fusion block, and replaces anchors with an anchor-free head followed by a CTC decoder. After mixed-precision compilation in Torch-TensorRT, the 38 MB model sustains 14 FPS on a Jetson Nano—73 % faster than the anchor-free AF-Net (8.1 FPS) and 49 % faster than YOLOv8-MobileLPR (9.5 FPS)—while keeping competitive accuracy (90.2 % mAP) and halving AF-Net’s power consumption (4.8 W vs 8.8 W). Light-Edge therefore satisfies the stringent speed–accuracy envelope required for large-scale, privacy-preserving ALPR on resource-constrained edge devices.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Real-Time Image Processing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11554-025-01738-3" target="_blank">https://dx.doi.org/10.1007/s11554-025-01738-3</a></p> |
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