BLPR: 通过置信度驱动的VLM回退实现视点和光照变化下的鲁棒车牌识别
BLPR: Robust License Plate Recognition under Viewpoint and Illumination Variations via Confidence-Driven VLM Fallback
AI总结：
本文提出BLPR框架，通过合成数据预训练和真实数据微调提升Bolivian车牌识别鲁棒性，引入轻量视觉语言模型作为置信度回退机制，实现89.6%的字符识别准确率。
摘要：
在非约束环境中实现鲁棒的车牌识别仍是一项重大挑战，特别是在数据匮乏且视觉特征独特的地区，如玻利维亚。现实条件下的识别精度常因光照变化和视角扭曲而下降。为解决这些问题，我们引入BLPR，一种专门针对玻利维亚车牌的深度学习基于车牌检测和识别（LPDR）框架。所提出的系统采用两阶段流程，首先在Blender中生成合成数据预训练YOLO检测器以模拟极端视角和光照条件，随后在拉巴斯收集的街道级数据上进行微调。检测到的车牌经过几何校正后传递给字符识别模型。为了提高在模糊场景下的鲁棒性，引入轻量级视觉语言模型（Gemma3 4B）作为置信度驱动的回退机制。此外，该框架进一步利用合成到真实域适应以提高在多样化现实条件下的鲁棒性。我们还引入了首个公开可用的玻利维亚LPDR数据集，使在多样化视角和光照条件下进行评估成为可能。系统在真实数据上实现了89.6%的字符识别准确率，证明了其在挑战性城市环境中的有效性。我们的项目在https://github.com/EdwinTSalcedo/BLPR上公开发布。
英文摘要：
Robust license plate recognition in unconstrained environments remains a significant challenge, particularly in underrepresented regions with limited data availability and unique visual characteristics, such as Bolivia. Recognition accuracy in real-world conditions is often degraded by illumination changes and viewpoint distortion. To address these challenges, we introduce BLPR, a deep learning-based License Plate Detection and Recognition (LPDR) framework designed for Bolivian license plates. BLPR adaptively applies geometric rectification, illumination correction, and VLM-assisted fallback based on image-condition and confidence cues. The proposed system uses a YOLO-based detector pretrained on synthetic data generated in Blender to simulate extreme perspectives and lighting conditions, and is fine-tuned on street-level data collected in La Paz, Bolivia. Detected plates are processed by a YOLO-based character recognizer, while a lightweight vision-language model (Gemma3 4B) is selectively triggered in ambiguous cases as a confidence-driven fallback mechanism. We also introduce the first publicly available Bolivian LPDR dataset for academic research, supporting evaluation under diverse viewpoint and illumination conditions. The system achieves a character-level recognition accuracy of 89.6% on real-world data, demonstrating its effectiveness for deployment in challenging urban environments.
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。
