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We present an improved and optimized MobileNetV3 model for accurately determining parking lot occupancy. CNRPark-EXT and PKLot datasets are used for training our model and incorporate modifications to enhance its performance. Using real-time video feed, our model divides individual parking space patches into busy or empty states. The modifications include using a different activation function, employing convolution block attention instead of the squeeze-and-excitation module, and utilizing blueprint separable convolutions. Comparative experiments with CarNet and mAlexNet demonstrate that our model achieves an average accuracy of 98.01%, surpassing previous state-of-the-art solutions. Our findings suggest the potential of the proposed model for real-time applications.

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Enhanced MobileNetV3: A Deep Learning Approach for Accurate Parking Lot Occupancy Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Yusufbek Yuldashev,
  • Sadriddin Khudoikulov,
  • Abrorjon Kucharov,
  • Rashid Nasimov,
  • Akmalbek Abdusalomov

摘要

We present an improved and optimized MobileNetV3 model for accurately determining parking lot occupancy. CNRPark-EXT and PKLot datasets are used for training our model and incorporate modifications to enhance its performance. Using real-time video feed, our model divides individual parking space patches into busy or empty states. The modifications include using a different activation function, employing convolution block attention instead of the squeeze-and-excitation module, and utilizing blueprint separable convolutions. Comparative experiments with CarNet and mAlexNet demonstrate that our model achieves an average accuracy of 98.01%, surpassing previous state-of-the-art solutions. Our findings suggest the potential of the proposed model for real-time applications.