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Roadside Parking Spaces Image Classification Using Deep Learning

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Advances in Computing Systems and Applications (CSA 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 199))

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Abstract

The shortage and imbalance of parking spaces have become serious problems in recent years on the roadside. Drivers may park incorrectly, which may take more parking space and disturb other drivers. To mitigate these problems, real-time parking monitoring with a detection system, is very indispensable. In this paper, we propose a classification system of images associated with the parking spaces for vehicles on a roadside based on the convolutional neuron networks (CNN). The image is supposed to be captured by a camera or other visual devices installed on the roadside and sent to a central computer for processing. The treatment is followed by a classification based on DL (deep learning) to determine the occupation of the parking spaces. The proposed classifier model uses fewer calculation parameters that involved a faster model by applying the principle of dilation. The proposed model has been evaluated and compared with the previous models using PKlot dataset, a set of well-known images publicly available. The evaluation results show the reliability of our proposal comparing with the previous works.

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Acknowledgment

This research is part of project entitled “Green and Smart Communication in Smart Cities”. The project is registered in the PRFU projects (Research Formation -University Projects) and approved by the Algerian Ministry of teaching and Scientific Research with the reference: C00L07UN160420180020.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University Djilali Bounaama, Khemis Miliana, Algeria

    Khalfi Ali

  2. Department of Electronics and Computer Science, University of Science and Technology Houari Boumediene, Bab Ezzouar, Algeria

    Guerroumi Mohamed

Authors
  1. Khalfi Ali
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  2. Guerroumi Mohamed
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Corresponding author

Correspondence to Khalfi Ali .

Editor information

Editors and Affiliations

  1. Ecole Militaire Polytechnique, Algiers, Algeria

    Mustapha Reda Senouci

  2. Ecole Militaire Polytechnique, Algiers, Algeria

    Mohamed El Yazid Boudaren

  3. Ecole Militaire Polytechnique, Algiers, Algeria

    Faouzi Sebbak

  4. Ecole Militaire Polytechnique, Algiers, Algeria

    M'hamed Mataoui

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Ali, K., Mohamed, G. (2021). Roadside Parking Spaces Image Classification Using Deep Learning. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_29

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