Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification
Keywords:
Convolutional Neural Network, Classification, Enhancement, Normalization Layer, Parking Space
Abstract
The research problem of this study is the urgent need for real-time parking availability information to assist drivers in quickly and accurately locating available parking spaces, aiming to improve upon the accuracy not achieved by previous studies. The objective of this research is to enhance the classification accuracy of parking spaces using a Convolutional Neural Network (CNN) model, specifically by integrating an effective normalizing function into the CNN architecture. The research method employed involves the application of four distinct normalizing functions to the EfficientParkingNet, a tailored CNN architecture designed for the precise classification of parking spaces. The results indicate that the EfficientParkingNet model, when equipped with the Group Normalization function, outperforms other models using Batch Normalization, Inter-Channel Local Response Normalization, and Intra-Channel Local Response Normalization in terms of classification accuracy. Furthermore, it surpasses other similar CNN models such as mAlexnet, you only look once (Yolo)+mobilenet, and CarNet in the same classification task. This demonstrates that EfficientParkingNet with Group Normalization significantly enhances parking space classification, thus providing drivers with more reliable and accurate parking availability information.
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References
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[25] Kaur, R. and Ranade, S.K., (2023). Improving accuracy of convolutional neural network-based skin lesion segmentation using group normalization and combined loss function. International Journal of Information Technology, 15(5), pp.2827-2835. https://doi.org/10.1007/s41870-023-01330-7.
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[27] Paidi, V., Fleyeh, H., Håkansson, J., & Nyberg, R. G. (2018). Smart parking sensors, technologies and applications for open parking lots: a review. IET Intelligent Transport Systems, 12(8), 735-741. https://doi.org/10.1049/iet-its.2018.5086.
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[29] M. Ahmad, S. F. Qadri, S. Qadri, I. A. Saeed, S. S. Zareen, Z. Iqbal, A. Alabrah, H. M. Alaghbari, and S. M. M. Rahman, (2022). A lightweight convolutional neural network model for liver segmentation in medical diagnosis. Computational Intelligence and Neuroscience, vol. 2022 https://doi.org/10.1155/2022/7954333
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[32] Srilakshmi, K. and Karthik, R., (2022). A Novel Method for Lip Movement Detection using Deep Neural Network. Journal of Scientific & Industrial Research, 81(06), pp.643-650.
[33] Fatihia, W.M., Fariza, A. and Karlita, T., (2023). CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification. JOIV: International Journal on Informatics Visualization, 7(1), pp.200-207. http://dx.doi.org/10.30630/joiv.7.1.1443
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[36] Chang, S., Park, H., Cho, J., Park, H., Yun, S. and Hwang, K., (2021). Subspectral normalization for neural audio data processing. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 850-854). IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413522.
[37] Zhou, X.Y., Sun, J., Ye, N., Lan, X., Luo, Q., Lai, B.L., Esperanca, P., Yang, G.Z. and Li, Z., (2020). Batch group normalization. arXiv preprint arXiv:2012.02782.
[2] R. Grbić and B. Koch, "Automatic vision-based parking slot detection and occupancy classification," Expert Systems with Applications, vol. 225, p. 120147, 2023. https://doi.org/10.1016/j.eswa.2023.120147.
[3] Jiang, X., & Ge, Z. (2021). Augmented multidimensional convolutional neural network for industrial soft sensing. IEEE Transactions on Instrumentation and Measurement, 70, 1-10.
[4] Medus, L. D., Saban, M., Francés-Víllora, J. V., Bataller-Mompeán, M., & Rosado-Muñoz, A. (2021). Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control, 125, 107962. https://doi.org/10.1016/j.foodcont.2021.107962.
[5] Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312-322.
[6] Singh, S. P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., & Gulyás, B. (2020). 3D deep learning on medical images: A review. Sensors, 20(18), 5097.
[7] Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019). Enhancing transportation systems via deep learning: A survey. Transportation Research Part C: Emerging Technologies, 99, 144-163.
[8] Rahman, S., Ramli, M., Arnia, F., Muharar, R., & Sembiring, A. (2021). Performance analysis of mAlexnet by training option and activation function tuning on parking images. IOP Conference Series: Materials Science and Engineering, 1087(1), 012084. https://doi.org/10.1088/1757-899X/1087/1/012084.
[9] Chen, L.-C., Sheu, R.-K., Peng, W.-Y., Wu, J.-H., & Tseng, C.-H. (2020). Video-Based Parking Occupancy Detection for Smart Control System. Applied Sciences, 10(3), 1079. https://doi.org/10.3390/app10031079.
[10] S. Rahman et al., “Mini shufflenet for efficient parking space classification,” in AIP Conference Proceedings, vol. 2480, no. 1, p. 030007, 2023, doi: http://doi.org/10.1063/5.0103430.
[11] Connie, T., Goh, M. K. O., Koo, V. C., Murata, K. T., & Phon-Amnuaisuk, S. (2021). Improved Parking Space Recognition via Grassmannian Deep Stacking Network with Illumination Correction. In International Conference on Computational Intelligence in Information System (pp. 150-159). Springer.
[12] Rahman, S., Ramli, M., Arnia, F., Muharar, R., Ikhwan, M., & Munzir, S. (2022). Enhancement of convolutional neural network for urban environment parking space classification. Global Journal of Environmental Science and Management, 8(3), 1-12. https://doi.org/10.22034/gjesm.2022.03.02.
[13] Rahman, S., Ramli, M., Arnia, F., Sembiring, A., & Muharar, R. (2020). Convolutional Neural Network Customization for Parking Occupancy Detection. In 2020 International Conference on Electrical Engineering and Informatics (ICELTICs) (pp. 1-6). https://doi.org/10.1109/ICELTICs50595.2020.9315509.
[14] H. Naseri and V. Mehrdad, (2023). Novel CNN with investigation on accuracy by modifying stride, padding, kernel size and filter numbers. Multimedia Tools and Applications, vol. 82, no. 15, pp. 23673-23691, Jun. 2023, doi: https://doi.org/10.1007/s11042-023-14603-x.
[15] Naseri, H., & Mehrdad, V. (2023). Novel CNN with investigation on accuracy by modifying stride, padding, kernel size and filter numbers. Multimedia Tools and Applications, 1-19.
[16] F. Alrasheedi, X. Zhong, and P. C. Huang, (2023). Padding module: Learning the padding in deep neural networks. IEEE Access, vol. 11, pp. 7348-7357, Jan. 2023, doi: https://10.1109/ACCESS.2023.3238315.
[17] R. Aruna, M. S. Devi, A. Anand, U. Dutta, and C. N. Sagar, (2023). Inception Nesterov Momentum Adam L2 Regularized Learning Rate CNN for Sugarcane Disease Classification. in 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pp. 1-4, doi: 10.1109/ICAECT57570.2023.10117792.
[18] Ajayi, O. G., & Ashi, J. (2023). Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme. Smart Agricultural Technology, 3, 100128.
[19] Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316. https://doi.org/10.1016/j.neucom.2020.07.061.
[20] Ismail, A., Ahmad, S. A., Soh, A. C., Hassan, K., & Harith, H. H. (2019). Improving convolutional neural network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification. International Journal of Integrated Engineering, 11(4).
[21] T. Katona, G. Tóth, M. Petró, and B. Harangi, (2024). Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification. Mathematics, vol. 12, no. 6, p. 806. https://doi.org/10.3390/math12060806.
[22] Wang, S.-H., Muhammad, K., Hong, J., Sangaiah, A. K., & Zhang, Y.-D. (2020). Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing and Applications, 32, 665-680.
[23] T.V. Lakshmi and C.V. Krishna Reddy, (2024). Classification of skin lesions by incorporating drop-block and batch normalization layers in representative CNN models. Arabian Journal for Science and Engineering, vol. 49, no. 3, pp. 3671-3684. https://doi.org/10.1007/s13369-023-08131-x.
[24] H.K. Dishar and L.A. Muhammed, (2023). A Review of the Overfitting Problem in Convolution Neural Network and Remedy Approaches. Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 15, no. 2, pp. 155. https://doi.org/10.29304/jqcm.2023.15.2.1240
[25] Kaur, R. and Ranade, S.K., (2023). Improving accuracy of convolutional neural network-based skin lesion segmentation using group normalization and combined loss function. International Journal of Information Technology, 15(5), pp.2827-2835. https://doi.org/10.1007/s41870-023-01330-7.
[26] Kumar, A. (2021). Effects of different normalization techniques on the convolutional neural network. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 201-204).
[27] Paidi, V., Fleyeh, H., Håkansson, J., & Nyberg, R. G. (2018). Smart parking sensors, technologies and applications for open parking lots: a review. IET Intelligent Transport Systems, 12(8), 735-741. https://doi.org/10.1049/iet-its.2018.5086.
[28] Wu, Z., & He, S. (2021). Improvement of the AlexNet networks for large-scale recognition applications. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(2), 493-503. https://doi.org/10.1007/s40998-020-00336-2.
[29] M. Ahmad, S. F. Qadri, S. Qadri, I. A. Saeed, S. S. Zareen, Z. Iqbal, A. Alabrah, H. M. Alaghbari, and S. M. M. Rahman, (2022). A lightweight convolutional neural network model for liver segmentation in medical diagnosis. Computational Intelligence and Neuroscience, vol. 2022 https://doi.org/10.1155/2022/7954333
[30] De, S. and Smith, S., (2020). Batch normalization biases residual blocks towards the identity function in deep networks. Advances in Neural Information Processing Systems, 33, pp.19964-19975.
[31] Meterez, A., Joudaki, A., Orabona, F., Immer, A., Rätsch, G., & Daneshmand, H. (2023). Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. arXiv preprint arXiv:2310.02012. https://arxiv.org/abs/2310.02012.
[32] Srilakshmi, K. and Karthik, R., (2022). A Novel Method for Lip Movement Detection using Deep Neural Network. Journal of Scientific & Industrial Research, 81(06), pp.643-650.
[33] Fatihia, W.M., Fariza, A. and Karlita, T., (2023). CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification. JOIV: International Journal on Informatics Visualization, 7(1), pp.200-207. http://dx.doi.org/10.30630/joiv.7.1.1443
[34] Dhanalakshmi, A. and Nagarajan, G., (2022). Group-normalized deep CNN-based in-loop filter for HEVC scalable extension. Signal, Image and Video Processing, 16(2), pp.437-445. https://doi.org/10.1007/s11760-021-01966-7
[35] Shao, J., Hu, K., Wang, C., Xue, X. and Raj, B., (2020). Is normalization indispensable for training deep neural network?. Advances in Neural Information Processing Systems, 33, pp.13434-13444.
[36] Chang, S., Park, H., Cho, J., Park, H., Yun, S. and Hwang, K., (2021). Subspectral normalization for neural audio data processing. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 850-854). IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413522.
[37] Zhou, X.Y., Sun, J., Ye, N., Lan, X., Luo, Q., Lai, B.L., Esperanca, P., Yang, G.Z. and Li, Z., (2020). Batch group normalization. arXiv preprint arXiv:2012.02782.
Published
2024-06-18
How to Cite
rahman, sayuti, Ramli, M., Sembiring, A., Zen, M., & Syah, R. B. (2024). Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(3), 555-566. https://doi.org/https://doi.org/10.30812/matrik.v23i3.3871
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Articles
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