Revising deep learning methods in parking lot occupancy detection Article Swipe

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Anastasia Martynova , M. Kuznetsov , Vadim Porvatov , Vladislav Tishin , Andrey Kuznetsov , Natalia Semenova , Ksenia G. Kuznetsova ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2306.04288

Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing drivers to search for available parking lots across regions of interest. The classic approach to this task is based on the application of neural network classifiers to camera records. However, existing systems demonstrate a lack of generalization ability and appropriate testing regarding specific visual conditions. In this study, we extensively evaluate state-of-the-art parking lot occupancy detection algorithms, compare their prediction quality with the recently emerged vision transformers, and propose a new pipeline based on EfficientNet architecture. Performed computational experiments have demonstrated the performance increase in the case of our model, which was evaluated on 5 different datasets.

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Computer Science

High-Occupancy Vehicle Lane

Artificial Intelligence

Architectural Engineering

Voltage

Programming Language

Electrical Engineering

Visual Arts

Mathematical Analysis

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Computer science Occupancy Pipeline (software) Artificial intelligence Generalization Architecture Machine learning Transformer Deep learning Task (project management) Artificial neural network Data mining Engineering Systems engineering Mathematics Art Architectural engineering Voltage Programming language Electrical engineering Visual arts Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2306.04288
PDF: https://arxiv.org/pdf/2306.04288
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Cited By: 1
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DOI: https://doi.org/10.48550/arxiv.2306.04288

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Title: Revising deep learning methods in parking lot occupancy detection

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Language: en

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Publication year: 2023

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Publication date: 2023-06-07

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Authors: Anastasia Martynova, M. Kuznetsov, Vadim Porvatov, Vladislav Tishin, Andrey Kuznetsov, Natalia Semenova, Ksenia G. Kuznetsova

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Landing page: https://arxiv.org/abs/2306.04288

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Concepts: Computer science, Occupancy, Pipeline (software), Artificial intelligence, Generalization, Architecture, Machine learning, Transformer, Deep learning, Task (project management), Artificial neural network, Data mining, Engineering, Systems engineering, Mathematics, Art, Architectural engineering, Voltage, Programming language, Electrical engineering, Visual arts, Mathematical analysis

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Cited by: 1

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countries_distinct_count	0
institutions_distinct_count	7
sustainable_development_goals[0].id	https://metadata.un.org/sdg/11
sustainable_development_goals[0].score	0.699999988079071
sustainable_development_goals[0].display_name	Sustainable cities and communities
citation_normalized_percentile