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

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 30×5 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 73× 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.

Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems

“…Likewise, Palossi et al [23] demonstrate realtime human tracking on a nano drone, mounted with GAP8, a RISC-V parallel platform from GreenWaves Technologies, but are constrained to a single class. Lamberti et al [24] propose a specialized low-power Automatic License Plate Recognition system executed on GAP8 at an approximate frequency of 1 Hz.…”

“…This shift has led to a surge of interest in various research areas, including architecture search, quantization techniques, and advanced inference engines tailored for resource-constrained devices [19]- [22]. MCUs are now being equipped with novel open-source energy-efficient cores, such as RISC-V cores, parallel processing engines, dedicated hardware accelerators, and specialized co-processors aimed at enabling efficient execution of complex machine learning tasks [23], [24].…”

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

“…1) Computing Efficiency: DNN based vision oriented systems such as object classification, 3D object detection and SLAM are usually computational intensive, high resource and energy consuming tasks. The computing complexity relatively increases for real-time applications when these larger weight DNN are implemented on the embedded systems with limited memory [153]. For example the currently deployed level 3 autonomous vehicles [150], [243] are mostly dependent on vision sensors systems and consumes significant resources in terms of memory and energy.…”