Volume 18, Issue 2 (Journal of Control, V.18, N.2 Summer 2024)
JoC 2024, 18(2): 85-94 |
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JavadiMoghaddam S, GHolamalinejad H. Real-time vehicle type recognition using a convolution and Haar wavelet pooling based classifier. JoC 2024; 18 (2) :85-94
URL: http://joc.kntu.ac.ir/article-1-990-en.html
URL: http://joc.kntu.ac.ir/article-1-990-en.html
1- Bozorgmehr University of Qaenat
Abstract: (1573 Views)
Over the past few years, real-time classification of vehicle types has become an increasingly popular and important topic, given its wide range of applications in traffic control and analysis. Among the various methods available for classifying car types, convolutional neural networks (CNNs) have emerged as particularly appealing. In this article, we introduce a new real-time CNN architecture that is specifically designed to detect different types of cars. This innovative structure incorporates several unique features, including novel network architecture and structural elements, as well as an advanced learning method based on the back-propagation algorithm. One key aspect of our proposed method is its use of feature extraction at three different locations within the network, which allows for more accurate and efficient classification of car types. To evaluate the performance of our approach, we conducted experiments on two popular datasets (IRVD and MIO-TCD), and compared our results against those obtained using traditional CNN structures. Our evaluation results demonstrate that our proposed CNN architecture outperforms existing approaches, achieving superior classification accuracy across a range of criteria. Overall, our work represents a significant advance in the field of real-time car type classification, with broad implications for traffic management and analysis.
Type of Article: Research paper |
Subject:
Special
Received: 2023/06/29 | Accepted: 2024/08/7 | ePublished ahead of print: 2024/09/14 | Published: 2024/09/20
Received: 2023/06/29 | Accepted: 2024/08/7 | ePublished ahead of print: 2024/09/14 | Published: 2024/09/20
References
1. [1] C. Zhang, H. Ho, W. H. Lam, W. Ma, S. Wong, and A. H. Chow, "Lane-based estimation of travel time distributions by vehicle type via vehicle re-identification using low-resolution video images," Journal of Intelligent Transportation Systems, pp. 1-20, 2022. [DOI:10.1080/15472450.2022.2027767]
2. [2] N. Arora, Y. Kumar, R. Karkra, and M. Kumar, "Automatic vehicle detection system in different environment conditions using fast R-CNN," Multimedia Tools and Applications, vol. 81, no. 13, pp. 18715-18735, 2022. [DOI:10.1007/s11042-022-12347-8]
3. [3] N. Arora and Y. Kumar, "Automatic vehicle detection system in Day and Night Mode: challenges, applications and panoramic review," Evolutionary Intelligence, pp. 1-19, 2022. [DOI:10.1007/s12065-022-00723-0]
4. [4] M. Anandhalli, A. Tanuja, and P. Baligar, "Geometric invariant features for the detection and analysis of vehicle," Multimedia tools and applications, vol. 81, no. 23, pp. 33549-33567, 2022. [DOI:10.1007/s11042-022-12919-8]
5. [5] R. S. El-Sayed and M. N. El-Sayed, "Classification of vehicles' types using histogram oriented gradients: comparative study and modification," IAES International Journal of Artificial Intelligence, vol. 9, no. 4, p. 700, 2020. [DOI:10.11591/ijai.v9.i4.pp700-712]
6. [6] A. Thomas, P. Harikrishnan, P. Palanisamy, and V. P. Gopi, "Moving vehicle candidate recognition and classification using inception-resnet-v2," in 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020: IEEE, pp. 467-472. [DOI:10.1109/COMPSAC48688.2020.0-207]
7. [7] H.-H. Jebamikyous and R. Kashef, "Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges," IEEE Access, vol. 10, pp. 10523-10535, 2022. [DOI:10.1109/ACCESS.2022.3144407]
8. [8] F. C. Soon, H. Y. Khaw, J. H. Chuah, and J. Kanesan, "Semisupervised PCA convolutional network for vehicle type classification," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8267-8277, 2020. [DOI:10.1109/TVT.2020.3000306]
9. [9] Z. Huo, Y. Xia, and B. Zhang, "Vehicle type classification and attribute prediction using multi-task RCNN," in 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016: IEEE, pp. 564-569. [DOI:10.1109/CISP-BMEI.2016.7852774]
10. [10] S. Awang, N. M. A. N. Azmi, and M. A. Rahman, "Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy," IEEE Access, vol. 8, pp. 14265-14277, 2020. [DOI:10.1109/ACCESS.2019.2963486]
11. [11] I. O. Joshua, M. O. Arowolo, M. O. Adebiyi, O. R. Oluwaseun, and K. A. Gbolagade, "Development of an Image Processing Techniques for Vehicle Classification Using OCR and SVM," in 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), 2023, vol. 1: IEEE, pp. 1-9. [DOI:10.1109/SEB-SDG57117.2023.10124622]
12. [12] R. I. Borman, Y. Fernando, and Y. E. P. Yudoutomo, "Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 339-345, 2022. [DOI:10.29207/resti.v6i2.3954]
13. [13] A. Kherraki and R. El Ouazzani, "Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring," IAES International Journal of Artificial Intelligence, vol. 11, no. 1, p. 110, 2022. [DOI:10.11591/ijai.v11.i1.pp110-120]
14. [14] X. Xu, J. Wu, J. Wang, Q. Qu, Z. Tan, and M. Luo, "Materials Identification of Polarized Pulse Laser Detection Based on Sparse Autoencoder and Softmax Classifier Framework," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9, 2022. [DOI:10.1109/TIM.2022.3212117]
15. [15] H. Gholamalinejad and H. Khosravi, "Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks," Expert Systems with Applications, vol. 183, p. 115420, 2021. [DOI:10.1016/j.eswa.2021.115420]
16. [16] E. Zhu, M. Xu, and D. C. Pi, "Vehicle Type Recognition Algorithm Based on Improved Network in Network," Complexity, vol. 2021, 2021. [DOI:10.1155/2021/6061939]
17. [17] Y. Zhou, "Vehicle image recognition using deep convolution neural network and compressed dictionary learning," Journal of Information Processing Systems, vol. 17, no. 2, pp. 411-425, 2021.
18. [18] H. Gholamalinejad and H. Khosravi, "Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks," Expert systems with Applications, vol. 183, 2021. [DOI:10.1016/j.eswa.2021.115420]
19. [19] D. Misra, "Mish: A self regularized non-monotonic activation function," arXiv preprint arXiv:1908.08681, 2019.
20. [20] E. C. Seyrek and M. Uysal, "A comparative analysis of various activation functions and optimizers in a convolutional neural network for hyperspectral image classification," Multimedia Tools and Applications, pp. 1-32, 2023. [DOI:10.1007/s11042-023-17546-5]
21. [21] H. Gholamalinejad and H. Khosravi, "Whitened gradient descent, a new updating method for optimizers in deep neural networks," Journal of AI and Data Mining, vol. 10, no. 4, pp. 467-477, 2022.
22. [22] W. Wang, P. Zhang, T. Lan, and V. Aggarwal, "Datacenter net profit optimization with deadline dependent pricing," in Information Sciences and Systems (CISS), 2012 46th Annual Conference on, 2012: IEEE, pp. 1-6. [DOI:10.1109/CISS.2012.6310925]
23. [23] M. L. McHugh, "Interrater reliability: the kappa statistic," Biochemia medica: Biochemia medica, vol. 22, no. 3, pp. 276-282, 2012. [DOI:10.11613/BM.2012.031]
24. [24] A. Krizhevsky and G. Hinton, "Learning multiple layers of features from tiny images," 2009.
25. [25] H. Gholamalinejad and H. Khosravi, "IRVD: A Large-Scale Dataset for Classification of Iranian Vehicles in Urban Streets," Journal of AI and Data Mining, pp. -, 2020, doi: 10.22044/jadm.2020.8438.1982.
26. [26] "COVID-19 CT Scan Images." https://www.kaggle.com/azaemon/preprocessed-ct-scans-for-covid19?select=Original+CT+Scans (accessed.
27. [27] W. Ning et al., "iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia," 2020. [DOI:10.21203/rs.3.rs-21834/v1]
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