Problem
Research questions and friction points this paper is trying to address.
License Plate Recognition
Low-Power MCU
Edge AI
RISC-V
Energy Efficiency
Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
🤖 AI Summary
This work addresses the challenge of deploying efficient automatic license plate recognition (ALPR) on resource-constrained microcontroller units (MCUs) without relying on dedicated hardware accelerators. Leveraging a 9-core RISC-V GAP8 processor and an ultra-low-power grayscale image sensor, the authors implement a full-pipeline edge-based ALPR system that integrates SSDlite-MobilenetV2 for license plate detection and LPRNet for character recognition. Through synergistic model compression and multi-core scheduling optimizations, this study achieves the first end-to-end deployment of multiple deep learning models on an MCU-class platform. Experimental results demonstrate a detection mAP of 38.9% and character recognition accuracy exceeding 99.13% on public datasets, with the capability to recognize plates as small as 30×5 pixels. The system operates at a power consumption of only 117 mW, achieving a 73× improvement in energy efficiency compared to the Raspberry Pi 3.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
RISC-V MCU
low-power ALPR
multi-model inference
edge vision system
energy-efficient deep learning
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