用视觉语言模型直接识别尼日利亚车牌，无需标注数据。
Evaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition
- 直接用大模型处理车牌图像，跳过传统检测+识别流程。
- 谷歌Gemini和通义千问在错误率上显著优于其他模型。
- 适合缺乏标注数据的低资源场景，对真实世界复杂环境更鲁棒。
车牌识别系统在交通监控、安保执法和城市出行管理中至关重要。传统LPR系统依赖于基于You Only Look Once（YOLO）的目标检测与光学字符识别（OCR）的多阶段流程，存在资源消耗高、非结构化环境表现差、需大量标注数据等问题。本研究探索视觉语言模型（VLMs）作为统一的零样本学习方案在尼日利亚车牌识别中的潜力。基于在尼日利亚收集的88张具有挑战性的实际图像数据集，评估了五种VLM：Gemini 2.0 Flash Exp（Google DeepMind）、Qwen2.5-VL-7B-Instruct（Alibaba）、GPT-4o（OpenAI）、Claude 4 Sonnet（Anthropic）和Llama 3.2 Vision 90b（Meta）。基于字符错误率（CER）的结果显示，Gemini和Qwen在复杂图像场景下的准确性和鲁棒性显著优于其他模型。该工作凸显了VLM相较于YOLO+OCR的实际优势，质疑了部分模型厂商的宣传，并对各VLM性能进行了比较。
原文摘要 · Abstract (English)
License Plate Recognition (LPR) systems are critical tools in traffic monitoring, security enforcement, and urban mobility management. Traditional LPR systems often rely on a multi-stage pipeline involving object detection using You Only Look Once (YOLO) and Optical Character Recognition (OCR), which suffer from limitations such as high resource demands, poor performance in unstructured environments, and the need for large annotated datasets. This study explores the potential of Vision-Language Models (VLMs) as a unified, zeroshot learning solution for Nigerian license plate recognition. Using a curated dataset of 88 challenging real-world images collected in Nigeria, we evaluate five selected VLMs: Gemini 2.0 Flash Exp (Google DeepMind), Qwen2.5-VL-7B-Instruct (Alibaba), GPT-4o (OpenAI), Claude 4 Sonnet (Anthropic), and Llama 3.2 Vision 90b (Meta). Results based on Character Error Rate (CER) reveal that Gemini and Qwen significantly outperform other models in both accuracy and robustness, on the challenging image scenarios. This work highlights the practical advantages of VLMs over YOLO+OCR, questions the claims by model providers, and compares the performances of the VLMs.
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