[Submitted on 7 Jul 2026]
Title:Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
View a PDF of the paper titled Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System, by Lorenzo Lamberti and 4 other authors
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Abstract:In this paper, we present the first (to the best of our knowledge) demonstration of a low-power MCU-based edge device for Automatic License Plate Recognition (ALPR). The design leverages on a 9-core RISC-V processor, GAP8, coupled with a QVGA ultra-low-power greyscale imager. The proposed visual processing pipeline uses a multi-model inference approach based on SSDlite-MobilenetV2 for license plate detection and LPRNet for optical character recognition, reaching a 38.9% mAP score for the first task and a recognition rate of >99.13% for the latter on public datasets. On real-world data, the pipeline recognizes registration numbers when the size of LP crops is as small as 30x5 pixels. Thanks to the applied compression and optimization strategies, the multi-model inference (687 MMAC) achieves a throughput of 1.09 FPS at a power cost of 117 mW when running on GAP8. Our solution is the first MCU-class device embedding such a level of network complexity, resulting to be 73x more energy-efficient w.r.t. precedent mobile-class ALPR system featuring a Raspberry Pi3. The proposed design does not resort to any hardwired acceleration engines, thus retaining full flexibility for future algorithmic improvements.
Comments: 5 pages, 2 figures, 5 tables. This paper has been accepted for publication in the IEEE International Symposium on Circuits and Systems (ISCAS). Copyright 2021 IEEE
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2607.09768 [cs.CV]
(or arXiv:2607.09768v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.09768
arXiv-issued DOI via DataCite
Submission history
From: Lorenzo Lamberti [view email] [v1] Tue, 7 Jul 2026 09:24:44 UTC (468 KB)
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