评估视觉语言模型作为尼日利亚车牌识别的零样本学习替代方案：对比YOLO与光学字符识别
Evaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition
AI总结：
研究探索视觉语言模型作为尼日利亚车牌识别的统一零样本解决方案，在88张真实图像上评估5种VLM，发现Gemini和Qwen在字符错误率上显著优于YOLO+OCR。
摘要：
车牌识别系统是交通监控、安全执法和城市交通管理中的关键工具。传统的车牌识别系统通常依赖于多阶段流水线，包括使用YOLO进行目标检测和光学字符识别，这些方法存在资源需求高、在非结构化环境中性能差以及需要大量标注数据集等局限性。本研究探索了视觉语言模型作为尼日利亚车牌识别的统一零样本学习解决方案的潜力。使用在尼日利亚收集的88张具有挑战性的真实世界图像数据集，我们评估了五种选定的VLM：Gemini 2.0 Flash Exp（Google DeepMind）、Qwen2.5-VL-7B-Instruct（阿里巴巴）、GPT-4o（OpenAI）、Claude 4 Sonnet（Anthropic）和Llama 3.2 Vision 90b（Meta）。基于字符错误率的结果显示，在具有挑战性的图像场景中，Gemini和Qwen在准确性和鲁棒性上显著优于其他模型。这项工作突出了VLM相对于YOLO+OCR的实际优势，质疑了模型提供商的声明，并比较了各VLM的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
