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Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network.

Sherzod Nurullayev1, Sang-Woong Lee2

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This study introduces a robust visual detection method using Dilated Convolutional Neural Networks for accurate vacant parking space identification. The approach offers a reliable and generalized solution for real-world parking management challenges.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Urban traffic congestion and the difficulty of finding parking are significant challenges.
  • Existing parking occupancy detection systems are often costly or lack generalization across different camera views.
  • Affordable visual detection methods are needed, leveraging existing camera infrastructure.

Purpose of the Study:

  • To develop a reliable and generalized visual detection method for parking space occupancy.
  • To address challenges like varying lighting, perspective distortion, occlusions, and seasonal changes.
  • To propose an approach using Dilated Convolutional Neural Networks for single parking spot image analysis.

Main Methods:

  • Utilized a Dilated Convolutional Neural Network (DCNN) for parking space occupancy detection.
  • Trained and evaluated the model on publicly available datasets (PKLot and CNRPark + EXT).
  • Compared the proposed method's performance against the AlexNet architecture.

Main Results:

  • The proposed DCNN approach demonstrated superior robustness and generalization capabilities compared to prior methods.
  • The system performed reliably even on unseen images from different camera viewpoints.
  • Achieved highly promising results, outperforming AlexNet in classifying parking spaces as vacant or occupied.

Conclusions:

  • The developed Dilated Convolutional Neural Network method provides a robust, stable, and well-generalized solution for parking space occupancy detection.
  • The approach is effective for unseen images and different camera viewpoints, indicating potential for broader application in various parking lots.
  • This visual detection system offers a cost-effective and reliable alternative for intelligent parking management.