基金项目

基于机器视觉的地下停车场车位检测 与路径规划研究

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  • 厦门大学嘉庚学院

网络出版日期: 2024-08-28

Research on Machine Vision-based Underground Parking Space Detection and Path Planning

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  • Xiamen University Tan Kah Kee College

Online published: 2024-08-28

摘要

在当今科技迅速发展的时代,智能停车场识别、定位和导航系统已成为商业综合体、医院等大型公共场所不可或 缺的一部分。传统停车场存在诸多问题,如信号弱、导航精度低以及停车位难以寻找等。为提高停车效率,设计了基于机器视 觉的车位探测与路径规划系统。该系统利用超声波传感器收集停车位信息,并通过YoloV5图像识别技术分析驶入车辆的车 牌信息。所有数据传送至后台服务器,再经由微信小程序呈现给客户。通过先进的路径规划算法,系统能确定离客户最近的 空余停车位,并提供导航服务。

本文引用格式

黄荣跃  陈岳龙  蔡政楠  陈 荣  罗 旭  范子琦 . 基于机器视觉的地下停车场车位检测 与路径规划研究[J]. 电脑与电信, 2024 , 1(5) : 10 . DOI: 10.15966/j.cnki.dnydx.2024.05.006

Abstract

In today's rapidly advancing technological era, intelligent parking lot recognition, positioning, and navigation systems have become an indispensable part of large public venues such as commercial complexes and hospitals. Traditional parking lots suf‐ fer from various issues including weak signals, low navigation accuracy, and difficulty in finding parking spaces. To enhance parking efficiency, we have designed a machine vision-based parking space detection and path planning system. This system utilizes ultra‐ sonic sensors to gather parking space information and employs YoloV5 image recognition technology to analyze the license plate in‐ formation of incoming vehicles. All data is transmitted to a backend server and then presented to customers through a WeChat miniprogram. Using advanced path planning algorithms, the system can identify the nearest available parking space to the customer and provide navigation services.
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