Smart parking systems help reduce congestion and minimize users' search time, thereby contributing to smart city adoption and enhancing urban mobility. In previous works, we presented a system developed on a university campus to monitor parking availability by estimating the number of free spaces from vehicle counts within a region of interest. Although this approach achieved good accuracy, it restricted the system's ability to provide spot-level insights and support more advanced applications. To overcome this limitation, we extend the system with a spot-wise monitoring strategy based on a distance-aware matching method with spatial tolerance, enhanced through an Adaptive Bounding Box Partitioning method for challenging spaces. The proposed approach achieves a balanced accuracy of 98.80% while maintaining an inference time of 8 seconds on a resource-constrained edge device, enhancing the capabilities of YOLOv11m, a model that has a size of 40.5 MB. In addition, two new components were introduced: (i) a Digital Shadow that visually represents parking lot entities as a base to evolve to a full Digital Twin, and (ii) an application support server based on a repurposed TV box. The latter not only enables scalable communication among cloud services, the parking totem, and a bot that provides detailed spot occupancy statistics, but also promotes hardware reuse as a step towards greater sustainability.

翻译：智能停车系统有助于缓解交通拥堵并缩短用户寻泊时间，从而推动智慧城市落地并提升城市交通效率。在前期工作中，我们提出了一套部署于大学校园的停车监测系统，通过统计感兴趣区域内车辆数量来估算空闲车位。该方法虽具有较高精度，但无法提供车位级洞察及支持更高级应用。为突破此限制，本研究提出基于空间容错距离感知匹配策略的车位级监测方案，并针对复杂场景引入自适应边界框分割方法进行增强。该方案在资源受限的边缘设备上实现了98.80%的平衡准确率，同时保持8秒的推理耗时，显著提升了参数量仅40.5MB的YOLOv11m模型性能。此外，系统引入两大新组件：（1）作为数字孪生演进基础的"数字影射"模块，可对停车场实体进行可视化表征；（2）基于改造电视盒构建的应用支持服务器。后者不仅实现了云服务、停车终端与提供详细车位统计数据的机器人之间的可扩展通信，还通过硬件复用机制推动了系统可持续性发展。