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Smart parking space detection under hazy conditions using convolutional neural networks: a novel approach

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Abstract

Limited urban parking space combined with urbanization has necessitated the development of smart parking systems that can communicate the availability of parking slots to the end-users. Towards this, various deep learning based solutions using convolutional neural networks have been proposed for parking space occupation detection. Though these approaches are robust to partial obstructions and lighting conditions, their performance is found to degrade in the presence of haze conditions. Looking in this direction, this paper investigates the use of dehazing networks that improves the performance of parking space occupancy classifier under hazy conditions. Additionally, training procedures are proposed for dehazing networks to maximize the performance of the system under both hazy and non-hazy conditions. The proposed system is deployable as part of existing smart parking systems, where a limited number of cameras are used to monitor hundreds of parking spaces. To validate our approach, we have developed a custom hazy parking system dataset from real-world task-driven test set of RESIDE-β dataset. The proposed approach is tested against existing state-of-the-art parking space detectors on CNRPark-EXT and hazy parking system datasets. Experimental results indicate a significant accuracy improvement of the proposed approach on the hazy parking system dataset.

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Data Availability

The dataset generated during and/or analysed during the current study is available from the corresponding author on reasonable request.

Notes

  1. Smart Parking Market worth 5.25 Billion USD by 2021. https://www.marketsandmarkets.com/PressReleases/smart-parking.asp

  2. “Beijing realtime weather photos,” http://goo.gl/svzxLm.

  3. Chollet, Franccois et al. “Keras.” https://keras.io. (2015).

  4. https://github.com/GauravS9776/Hazy-parking-system

  5. https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/

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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Gaurav Satyanath

  2. Department of Mathematics, BITS Pilani K K Birla Goa Campus, Goa, India

    Jajati Keshari Sahoo

  3. Department of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India

    Rajendra Kumar Roul

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  1. Gaurav Satyanath
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Satyanath, G., Sahoo, J.K. & Roul, R.K. Smart parking space detection under hazy conditions using convolutional neural networks: a novel approach. Multimed Tools Appl 82, 15415–15438 (2023). https://doi.org/10.1007/s11042-022-13958-x

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