Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System

Researchers demonstrated the first low-power microcontroller-based edge device for automatic license plate recognition, using a 9-core RISC-V processor and ultra-low-power imager. The system achieved 38.9% mAP for detection and >99.13% recognition rate while consuming only 117 mW, making it 73x more energy-efficient than a Raspberry Pi3-based system.

arXiv:2607.09768v1 Announce Type: new 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.