Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification
DOI:
https://doi.org/10.30812/matrik.v23i3.3871Keywords:
Convolutional Neural Network, Classification, Enhancement, Normalization Layer, Parking SpaceAbstract
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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[2] A. Kamilaris and F. X. Prenafeta-Bold´u, “A review of the use of convolutional neural networks in agriculture,†The Journal of
Agricultural Science, vol. 15, no. 3, pp. 312–322, 2021.
[3] S. P. Singh, L. Wang, S. Gupta, H. Goli, P. Padmanabhan, and B. Guly´as, “3D deep learning on medical images: a review,â€
Sensors, vol. 20, no. 18, pp. 5097–5107, 2020.
[4] G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo, “Deep learning for decentralized parking lot occupancy
detection,†Expert Systems with Applications, vol. 72, no. 2, pp. 327–334, apr 2020, https://doi.org/10.1016/j.eswa.2016.10.055.
[5] L.-C. Chen, R.-K. Sheu,W.-Y. Peng, J.-H.Wu, and C.-H. Tseng, “Video-Based Parking Occupancy Detection for Smart Control
System,†Applied Sciences, vol. 10, no. 3, pp. 1079–1089, feb 2020, https://doi.org/10.3390/app10031079.
[6] S. Nurullayev and S.-W. Lee, “Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network,â€
Sensors, vol. 19, no. 2, pp. 277–287, jan 2020, https://doi.org/10.3390/s19020277.
[7] T. Connie, M. K. O. Goh, V. C. Koo, K. T. Murata, and S. Phon-Amnuaisuk, “Improved Parking Space Recognition via Grassmannian
Deep Stacking Network with Illumination Correction,†in International Conference on Computational Intelligence in
Information System. Springer, 2021, vol. 17, no. 1, pp. 150–159, https://doi.org/10.1007/978-3-030-68133-3 15.
[8] S. Rahman, M. Ramli, F. Arnia, R. Muharar, M. Ikhwan, and S. Munzir, “Enhancement of convolutional neural network for
urban environment parking space classification,†Global Journal of Environmental Science and Management, vol. 8, no. 3, pp.
1–12, 2022, https://doi.org/10.22034/gjesm.2022.03.02.
[9] H. Naseri and V. Mehrdad, “Novel CNN with investigation on accuracy by modifying stride, padding, kernel size and filter
numbers,†Multimedia Tools and Applications, pp. 1–19, 2023.
[10] T. Takase, S. Oyama, and M. Kurihara, “Effective neural network training with adaptive learning rate based on training loss,â€
Neural Networks, vol. 101, no. 1, pp. 68–78, 2020.
[11] O. G. Ajayi and J. Ashi, “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, vol. 3, pp. 100–128, 2023.
[12] X. Yin, W. Chen, X. Wu, and H. Yue, “Fine-tuning and visualization of convolutional neural networks,†in 2017 12th IEEE
Conference on Industrial Electronics and Applications (ICIEA), vol. 1, no. 2. IEEE, 2020, pp. 1310–1315.
[13] A. Ismail, S. A. Ahmad, A. C. Soh, K. Hassan, and H. H. Harith, “Improving convolutional neural network (CNN) architecture
(miniVGGNet) with batch normalization and learning rate decay factor for image classification,†International Journal of
Integrated Engineering, vol. 11, no. 4, 2021.
[14] A. Kumar, “Effects of different normalization techniques on the convolutional neural network,†in 2021 8th International Conference
on Computing for Sustainable Global Development (INDIACom), vol. 17, no. 1. IEEE, 2021, pp. 201–204.
[15] E. Chai, M. Pilanci, and B. Murmann, “Separating the effects of batch normalization on cnn training speed and stability using
classical adaptive filter theory,†in 2020 54th Asilomar Conference on Signals, Systems, and Computers, vol. 25, no. 20. IEEE,
2020, pp. 1214–1221.
[16] P. R. de Almeida, L. S. Oliveira, A. S. Britto, E. J. Silva, and A. L. Koerich, “PKLot A robust dataset for parking lot classification,â€
Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, jul 2022, https://doi.org/10.1016/j.eswa.2015.02.009.
[17] D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Instance normalization: The missing ingredient for fast stylization,†arXiv preprint
arXiv:1607.08022, vol. 5, no. 1, 2020.
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CNN-filters fine-tuning,†arXiv preprint arXiv:1904.03949, vol. 2, no. 1, 2020.
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Published
2024-06-18
Issue
Section
Articles
How to Cite
[1]
sayuti rahman, M. Ramli, A. Sembiring, M. Zen, and R. B. Syah, “Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification”, MATRIK, vol. 23, no. 3, pp. 557–568, Jun. 2024, doi: 10.30812/matrik.v23i3.3871.
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