基于YOLO的低功耗车牌识别系统，实现在城市巡逻车上的实时精准识别。
PatrolVision: Automated License Plate Recognition in the wild
- 构建基于RFB-Net与自研YOLO网络的端到端车牌检测与字符识别流程。
- 在16000+图像数据集上实现86%检测精度、67%字符识别率，部分匹配率达89%。
- 支持多角度扭曲车牌处理，可在特斯拉P4 GPU上达到64帧/秒的实时性能。
由于准确性和处理速度在大规模应用中的局限，人工智能技术在公共服务中的采纳率仍较低。尽管计算机视觉在自动驾驶等领域表现强劲，但其在交通监控中的应用并未广泛普及。现有大量学术研究针对自动车牌识别（ALPR）系统，但很少提供适用于城市巡逻场景的完整解决方案。本文提出一种面向新加坡车牌的低功耗GPU部署原型系统，专用于城市巡逻车辆的自动化车辆检测、识别与跟踪。我们设计了基于YOLO的网络模型，支持单行与双行车牌识别，并聚焦于真实世界中因视角倾斜导致的车牌严重畸变问题。首先通过RFB-Net检测图像中的车牌并进行多幅畸变车牌的几何校正；随后将校正后的车牌图像输入模型完成字符识别。我们在新构建的数据集（超过16,000张图像）上评估系统性能，结果显示：车牌检测精度为86%，字符识别正确率为67%；若允许一个字符错误，则整体匹配准确率达89%。系统在特斯拉P4 GPU上实测延迟满足64帧/秒的实时要求。
原文摘要 · Abstract (English)
Adoption of AI driven techniques in public services remains low due to challenges related to accuracy and speed of information at population scale. Computer vision techniques for traffic monitoring have not gained much popularity despite their relative strength in areas such as autonomous driving. Despite large number of academic methods for Automatic License Plate Recognition (ALPR) systems, very few provide an end to end solution for patrolling in the city. This paper presents a novel prototype for a low power GPU based patrolling system to be deployed in an urban environment on surveillance vehicles for automated vehicle detection, recognition and tracking. In this work, we propose a complete ALPR system for Singapore license plates having both single and double line creating our own YOLO based network. We focus on unconstrained capture scenarios as would be the case in real world application, where the license plate (LP) might be considerably distorted due to oblique views. In this work, we first detect the license plate from the full image using RFB-Net and rectify multiple distorted license plates in a single image. After that, the detected license plate image is fed to our network for character recognition. We evaluate the performance of our proposed system on a newly built dataset covering more than 16,000 images. The system was able to correctly detect license plates with 86\% precision and recognize characters of a license plate in 67\% of the test set, and 89\% accuracy with one incorrect character (partial match). We also test latency of our system and achieve 64FPS on Tesla P4 GPU
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