Visual Parking Occupancy Detection Using Extended Contextual Image Information via a Multi-Branch Output ConvNeXt Network

Along with society's development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved i...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (6), p.3329
Main Authors: Encío, Leyre, Díaz, César, Del-Blanco, Carlos R, Jaureguizar, Fernando, García, Narciso
Format: Article
Language:English
Subjects:
Algorithms
Automobiles
Cameras
Color imagery
Computer vision
ConvNeXt
convolutional neural networks
Data mining
Deep learning
detection
Ecological footprint
Methods
Metropolitan areas
Neural networks
Occupancy
Parking
parking lot
Sensors
Support vector machines
Urban areas
Vehicles
Vision systems
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Summary:Along with society's development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver's health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23063329