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基于自动学习与人工视觉的空车位定位专家系统设计

Expert system design for vacant parking space location using automatic learning and artificial vision.

作者信息

Carrera García Juan Manuel, Recas Piorno Joaquín, Guijarro Mata-García María

机构信息

UCM: Universidad Complutense de Madrid, Madrid, Spain.

出版信息

Multimed Tools Appl. 2022;81(27):38661-38683. doi: 10.1007/s11042-022-12906-z. Epub 2022 Apr 26.

DOI:10.1007/s11042-022-12906-z
PMID:35493418
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9038519/
Abstract

Finding a free parking space nowadays is a recurring problem in increasingly crowded public parking lots. The present study offers a solution that is based on the analysis of zenith images using artificial vision and is capable of automatically analyzing both the available spaces in the parking lot and their real-time occupancy. In an initial phase, the presented system semi-automatically detects the available parking spaces by filtering, thresholding, and carrying out a process of extracting the contour and approximating to a polygon the parking spaces of an empty parking lot. Once the size and location of the parking spaces have been mapped, the system is capable of detecting not only the presence of a vehicle in a parking space, but also the area of the parking space occupied by it with an accuracy of 98.21% using Region-based Convolutional Neural Networks. This feature allows the system to specify the appropriate parking space for a new vehicle entering the parking lot based on its specific dimensions and the correct location of the cars parked in the spaces adjacent to the free space.

摘要

如今，在日益拥挤的公共停车场找到一个免费停车位是一个反复出现的问题。本研究提供了一种基于使用人工视觉分析天顶图像的解决方案，该方案能够自动分析停车场中的可用车位及其实时占用情况。在初始阶段，所提出的系统通过滤波、阈值处理以及对空停车场的停车位进行轮廓提取和多边形逼近的过程，半自动地检测可用停车位。一旦确定了停车位的大小和位置，该系统不仅能够检测停车位中是否有车辆，还能够使用基于区域的卷积神经网络以98.21%的准确率检测车辆占用的停车位面积。这一特性使系统能够根据新进入停车场车辆的具体尺寸以及停在相邻空闲车位旁车辆的正确位置，为其指定合适的停车位。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/9eaee701d119/11042_2022_12906_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/9b074cf5a425/11042_2022_12906_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/36ea061f1623/11042_2022_12906_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/6d3fdb03deea/11042_2022_12906_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/d21405d95d1f/11042_2022_12906_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/4aab26dac396/11042_2022_12906_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/9eaee701d119/11042_2022_12906_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/2c713deac008/11042_2022_12906_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/3b83dc086ca6/11042_2022_12906_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/739bc243f208/11042_2022_12906_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/68b9217bf349/11042_2022_12906_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/fb5d01658827/11042_2022_12906_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/4ed6e18098ca/11042_2022_12906_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/9b074cf5a425/11042_2022_12906_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/36ea061f1623/11042_2022_12906_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/6d3fdb03deea/11042_2022_12906_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/d21405d95d1f/11042_2022_12906_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/4aab26dac396/11042_2022_12906_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce21/9038519/9eaee701d119/11042_2022_12906_Fig12_HTML.jpg

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