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Related Experiment Video

Updated: Aug 5, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Leyre Encío¹, César Díaz¹, Carlos R Del-Blanco¹

¹Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel computer vision system using deep learning to accurately detect vacant parking spaces, even in difficult conditions. The new method improves parking efficiency and reduces urban driving stress.

Keywords:

ConvNeXt computer vision convolutional neural networks deep learning detection parking parking lot

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Area of Science:

Computer Vision
Artificial Intelligence
Urban Planning

Background:

Increasing urbanization and vehicle numbers create significant challenges in finding parking in metropolitan areas.
Inefficient parking contributes to increased accidents, carbon footprint, and driver stress.
Technological solutions for real-time parking management are crucial for urban mobility.

Purpose of the Study:

To propose a novel computer-vision-based system for detecting vacant parking spaces.
To enhance the accuracy and robustness of parking detection in challenging environmental conditions.
To improve the efficiency of the urban parking process through advanced technology.

Main Methods:

Development of a new deep-learning algorithm based on a multi-branch output neural network.
Utilizing color imagery for parking space occupancy inference.
Maximizing contextual image information by considering the entire input image for each parking slot.

Main Results:

The proposed system demonstrates high robustness to varying illumination, camera perspectives, and occlusions.
The multi-branch neural network effectively infers parking space occupancy.
Extensive evaluations on public datasets show superior performance compared to existing approaches.

Conclusions:

The novel deep-learning system offers a robust and accurate solution for real-time vacant parking detection.
This technology can significantly improve urban parking management and driver experience.
The approach advances the field of computer vision for intelligent transportation systems.