按车位智能停车：集成YOLOv11和数字孪生的边缘计算架构

Automated Parking Space Detection Using Convolutional Neural Networks

Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a real-time parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whethe…

真实场景下的大气噪声鲁棒图像分类：混合CNN与Pin-GTSVM

Atmospheric Noise-Resilient Image Classification in a Real-World Scenario: Using Hybrid CNN and Pin-GTSVM

针对雾天停车位分类性能下降问题，提出混合CNN与Pin-GTSVM模型，无需去雾模块且抗大气噪声，在多个数据集上显著提升准确率。

使用卷积神经网络通过人工智能实现停车位地面真值测试自动化

Parking Space Ground Truth Test Automation by Artificial Intelligence Using Convolutional Neural Networks

本研究通过应用卷积神经网络等机器学习方法自动化停车位地面真值测试过程，优化实时街道停车服务，实现人力资源时间减少高达99.58%。

基于计算机视觉的车辆分配系统使用透视映射

Computer Vision-Based Vehicle Allotment System using Perspective Mapping

本研究提出利用计算机视觉和YOLOv8模型，通过逆透视映射实现高效车辆分配系统，提升智能停车效率。

基于集成的自监督学习方法用于数据有限场景下的停车位分类

An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data

针对数据有限场景下停车位分类任务，提出基于异构自编码器集成的自监督学习方法，可降低标注需求、提升域偏移下的鲁棒性，在PKLot和CNRPark基准上实现93%-96%的准确率。

The Smart Parking Management System

With growing, Car parking increases with the number of car users. With the increased use of smartphones and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the server and are recovered by the mobile application which offers many options…

基于图像分割与处理的停车位有效分析

Image Segmentation and Processing for Efficient Parking Space Analysis

在本文中，我们开发了一种方法来检测空置车位的环境中不明确的部分和轮廓的帮助下，MATLAB图像处理功能。由于停车位存在异常，例如照明不均匀，插槽线扭曲和汽车重叠。目前的常规算法难以处理图像以获得准确的结果。该算法采用图像预处理和伪轮廓检测相结合的技术，提高了检测效率。该方法还消除了使用单个传感器来检测汽车的需要，而是使用实时静态图像来考虑一组插槽，而不是通常的单个插槽方法。这大大降低了设计高效停车系统所需的费用。我们比较我们的算法的性能，其他技术。这些比较表明，所提出的算法可以检测停车位中的空位，而忽略了虚假数据和其他失真。

基于车辆出现的停车位检测

Vehicle Occurrence-based Parking Space Detection

针对现有停车场视觉管理方法依赖手动标注停车位、部署成本高的问题，提出一种基于实例分割与车辆出现热力图的自动停车位检测方法，在PKLot等数据集上取得了较高的AP得分。

Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system's scalability. Additionally, most of the approaches use deep learning models, making them not error-free and not reliable at scale. Thus, we propose an end-to-end smart camera parking system where we provide an autonomous de…

深度单一模型与集成模型：快速部署停车监控系统的洞见

Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems

本文研究跨场景即用型停车监控框架，通过多数据集、深度架构、融合与集成实验，证明在多样化公开数据上训练的单一模型无需目标场地标注即可达到95%准确率。