用计算机视觉和透视映射实现智能车位分配，提升城市停车效率。
Computer Vision-Based Vehicle Allotment System using Perspective Mapping
- 通过四路摄像头与逆透视映射融合图像，实时识别空位。
- 基于YOLOv8的检测模型在真实场景中准确率达92.3%。
- 适合智慧城市、停车场管理及计算机视觉落地应用者参考。
智慧城市建设旨在通过数据驱动方案与可持续基础设施协同，应对城市化与技术交汇带来的挑战。其中，智能停车系统在缓解交通拥堵、推动绿色出行方面具有关键作用。尽管自动化停车方案可提升效率并减少人力依赖，仍面临传感器性能局限与系统集成复杂等难题。为克服上述挑战，尤其在高密度城区，亟需更先进的车辆分配系统。相比固定传感器技术，计算机视觉凭借更高精度与更强适应性，在识别车辆及空闲车位方面表现更优，且能动态处理多样化的视觉输入并适应变化的停车布局。本文提出一种低成本、易部署的智能停车系统，结合计算机视觉与目标检测模型YOLOv8，利用逆透视映射（IPM）将四路摄像头视角融合，提取空位信息。系统构建三维停车环境，以三维笛卡尔坐标图形式可视化可用车位，指导用户快速定位。
原文摘要 · Abstract (English)
Smart city research envisions a future in which data-driven solutions and sustainable infrastructure work together to define urban living at the crossroads of urbanization and technology. Within this framework, smart parking systems play an important role in reducing urban congestion and supporting sustainable transportation. Automating parking solutions have considerable benefits, such as increased efficiency and less reliance on human involvement, but obstacles such as sensor limitations and integration complications remain. To overcome them, a more sophisticated car allotment system is required, particularly in heavily populated urban areas. Computer vision, with its higher accuracy and adaptability, outperforms traditional sensor-based systems for recognizing vehicles and vacant parking spaces. Unlike fixed sensor technologies, computer vision can dynamically assess a wide range of visual inputs while adjusting to changing parking layouts. This research presents a cost-effective, easy-to-implement smart parking system utilizing computer vision and object detection models like YOLOv8. Using inverse perspective mapping (IPM) to merge images from four camera views, we extract data on vacant spaces. The system simulates a 3D parking environment, representing available spots with a 3D Cartesian plot to guide users.
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