用智能机器人实时识别违停并自动发消息提醒，无需预处理车牌图像。
Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
- 用GPT-4o模型直接识别无预处理的车牌，免去复杂图像处理步骤。
- 在模拟停车场中实现平稳移动与自动视角调整，成功识别违规停车。
- 适用于室内停车场场景，兼具实用性与社会服务功能。
本研究设计了一款用于实时识别和通知违停的社会助手机器人。采用双模型对比方法，最终选用GPT-4o多模态模型完成车牌识别，无需图像预处理。实验中，机器人在模拟停车场内平稳行驶，并自动调整摄像头角度以捕捉周围环境图像。通过该模型识别车牌号码并判断其合法性；一旦发现违停，立即通过Line发送通知给系统管理员。该工作验证了多模态深度学习方法在车牌识别中的高精度表现，同时提供了一款可应用于实际场景（如室内停车场）的社会助手机器人解决方案。
原文摘要 · Abstract (English)
In the study, the social robot act as a patrol to recognize and notify illegal parking in real-time. Dual-model pipeline method and large multimodal model were compared, and the GPT-4o multimodal model was adopted in license plate recognition without preprocessing. For moving smoothly on a flat ground, the robot navigated in a simulated parking lot in the experiments. The robot changes angle view of the camera automatically to capture the images around with the format of license plate number. From the captured images of the robot, the numbers on the plate are recognized through the GPT-4o model, and identifies legality of the numbers. When an illegal parking is detected, the robot sends Line messages to the system manager immediately. The contribution of the work is that a novel multimodal deep learning method has validated with high accuracy in license plate recognition, and a social assistive robot is also provided for solving problems in a real scenario, and can be applied in an indoor parking lot.
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