用RISC-V多核芯片实现低功耗车牌识别，效率比树莓派高73倍。
Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
- 采用SSDlite-MobilenetV2与LPRNet双模型检测识别车牌。
- 在真实数据上最小30x5像素仍能识别，准确率超99.13%。
- 无专用加速器，可灵活升级算法，适合嵌入式场景。
本文首次（据我们所知）展示了基于低功耗MCU的边缘设备在自动车牌识别（ALPR）中的应用。该系统采用9核RISC-V处理器GAP8和超低功耗灰度图像传感器（QVGA），构建了多模型推理流程：使用SSDlite-MobilenetV2进行车牌检测，使用LPRNet进行字符识别。在公开数据集上，检测任务达到38.9% mAP，识别准确率超过99.13%。在真实数据中，当车牌裁剪尺寸小至30x5像素时仍可成功识别。通过压缩与优化策略，多模型推理（687 MMAC）在GAP8上实现1.09 FPS吞吐量，功耗仅117 mW。本方案是首个嵌入此类网络复杂度的MCU级设备，相比此前基于Raspberry Pi3的移动级系统，能效提升73倍。设计未依赖任何硬件加速单元，保留未来算法迭代的全部灵活性。
原文摘要 · Abstract (English)
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.
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