基于YOLOv8、YOLOv9、YOLOv10和YOLOv11的像素级感兴趣区域选择车辆检测智能停车系统
Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11
AI总结：
针对传统智能停车检测方案的局限，本研究融合物联网、边缘计算与深度学习，采用多款最新YOLO模型并提出像素级后处理ROI选择方法，在自定义数据集上实现99.68%平衡准确率，打造高精度、低成本且保护隐私的智能停车方案。
摘要：
城市化进程加快与城市车辆数量不断增长，凸显了高效停车管理系统的必要性。传统智能停车解决方案通常依赖传感器或摄像头进行车位占用检测，两类方案各有局限。近年来深度学习的发展催生了新的YOLO模型（YOLOv8、YOLOv9、YOLOv10和YOLOv11），但这些模型尚未在智能停车系统场景下得到广泛评估，尤其是结合感兴趣区域（Region of Interest, ROI）选择进行目标检测的场景。现有方法仍依赖固定多边形ROI选择或简单的基于像素的修改，限制了灵活性与精度。本研究提出一种融合物联网、边缘计算与深度学习概念的新方法，采用最新的YOLO模型进行车辆检测。通过对比边缘计算与云计算两种模式，研究发现边缘设备上的推理时间在1到92秒之间，具体取决于硬件配置与模型版本。此外，本文提出一种全新的像素级后处理ROI选择方法，可精准识别感兴趣区域，用于统计停车场图像中的车辆数量。该系统在包含3484张图像的自定义数据集上取得了99.68%的平衡准确率，提供了一种兼具成本效益的智能停车方案，既能保证车辆检测精度，又能保障数据隐私。
英文摘要：
The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new YOLO models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11), but these models have not been extensively evaluated in the context of smart parking systems, particularly when combined with Region of Interest (ROI) selection for object detection. Existing methods still rely on fixed polygonal ROI selections or simple pixel-based modifications, which limit flexibility and precision. This work introduces a novel approach that integrates Internet of Things, Edge Computing, and Deep Learning concepts, by using the latest YOLO models for vehicle detection. By exploring both edge and cloud computing, it was found that inference times on edge devices ranged from 1 to 92 seconds, depending on the hardware and model version. Additionally, a new pixel-wise post-processing ROI selection method is proposed for accurately identifying regions of interest to count vehicles in parking lot images. The proposed system achieved 99.68% balanced accuracy on a custom dataset of 3,484 images, offering a cost-effective smart parking solution that ensures precise vehicle detection while preserving data privacy
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。
