Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems

Manal Abdullah Alohali; Mashael Maashi; Raji Faqih; Hany Mahgoub; Abdullah Mohamed; Mohammed Assiri; Suhanda Drar; Manal Abdullah Alohali; Mashael Maashi; Raji Faqih; Hany Mahgoub; Abdullah Mohamed; Mohammed Assiri; Suhanda Drar

doi:10.3934/era.2023188

Electronic Research Archive

2023, Volume 31, Issue 7: 3704-3721. doi: 10.3934/era.2023188

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Research article

Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems

1.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2.
Department of Software Engineering, College of Computer and Information Science, King Saud University, Po box 103786, Riyadh 11543, Saudi Arabia
3.
Department of Business Administration, Applied College, King Khalid University, Mohail Asser, Saudi Arabia
4.
Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5.
Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
6.
Department of Computer Science, College of Sciences and Humanities- Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi Arabia
7.
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Academic Editor: Hans Wang

Received: 02 January 2023 Revised: 09 March 2023 Accepted: 10 March 2023 Published: 26 April 2023

Traffic surveillance systems are utilized to collect and monitor the traffic condition data of the road networks. This data plays a crucial role in a variety of applications of the Intelligent Transportation Systems (ITSs). In traffic surveillance, it is challenging to achieve accurate vehicle detection and count the vehicles from traffic videos. The most notable difficulties include real-time system operations for precise classification, identification of the vehicles' location in traffic flows and functioning around total occlusions that hamper the vehicle tracking process. Conventional video-related vehicle detection techniques such as optical flow, background subtraction and frame difference have certain limitations in terms of efficiency or accuracy. Therefore, the current study proposes to design the spotted hyena optimizer with deep learning-enabled vehicle counting and classification (SHODL-VCC) model for the ITSs. The aim of the proposed SHODL-VCC technique lies in accurate counting and classification of the vehicles in traffic surveillance. To achieve this, the proposed SHODL-VCC technique follows a two-stage process that includes vehicle detection and vehicle classification. Primarily, the presented SHODL-VCC technique employs the RetinaNet object detector to identify the vehicles. Next, the detected vehicles are classified into different class labels using the deep wavelet auto-encoder model. To enhance the vehicle detection performance, the spotted hyena optimizer algorithm is exploited as a hyperparameter optimizer, which considerably enhances the vehicle detection rate. The proposed SHODL-VCC technique was experimentally validated using different databases. The comparative outcomes demonstrate the promising vehicle classification performance of the SHODL-VCC technique in comparison with recent deep learning approaches.
- traffic surveillance,
- intelligent transportation systems,
- deep learning,
- RetinaNet,
- Vehicle detection,
- Vehicle classification
Citation: Manal Abdullah Alohali, Mashael Maashi, Raji Faqih, Hany Mahgoub, Abdullah Mohamed, Mohammed Assiri, Suhanda Drar. Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems[J]. Electronic Research Archive, 2023, 31(7): 3704-3721. doi: 10.3934/era.2023188

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Abstract

Traffic surveillance systems are utilized to collect and monitor the traffic condition data of the road networks. This data plays a crucial role in a variety of applications of the Intelligent Transportation Systems (ITSs). In traffic surveillance, it is challenging to achieve accurate vehicle detection and count the vehicles from traffic videos. The most notable difficulties include real-time system operations for precise classification, identification of the vehicles' location in traffic flows and functioning around total occlusions that hamper the vehicle tracking process. Conventional video-related vehicle detection techniques such as optical flow, background subtraction and frame difference have certain limitations in terms of efficiency or accuracy. Therefore, the current study proposes to design the spotted hyena optimizer with deep learning-enabled vehicle counting and classification (SHODL-VCC) model for the ITSs. The aim of the proposed SHODL-VCC technique lies in accurate counting and classification of the vehicles in traffic surveillance. To achieve this, the proposed SHODL-VCC technique follows a two-stage process that includes vehicle detection and vehicle classification. Primarily, the presented SHODL-VCC technique employs the RetinaNet object detector to identify the vehicles. Next, the detected vehicles are classified into different class labels using the deep wavelet auto-encoder model. To enhance the vehicle detection performance, the spotted hyena optimizer algorithm is exploited as a hyperparameter optimizer, which considerably enhances the vehicle detection rate. The proposed SHODL-VCC technique was experimentally validated using different databases. The comparative outcomes demonstrate the promising vehicle classification performance of the SHODL-VCC technique in comparison with recent deep learning approaches.

References

[1]	V. Kocur, M. Ftacnik, Multi-class multi-movement vehicle counting based on CenterTrack, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, 4009–4015.
[2]	J. Mirthubashini, V. Santhi, Video based vehicle counting using deep learning algorithms, in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, (2020), 142–147.
[3]	C. J. Lin, S. Y. Jeng, H. W. Lioa, A real-time vehicle counting, speed estimation, and classification system based on virtual detection zone and YOLO, Math. Probl. Eng., 2021 (2021), 1–10. https://doi.org/10.1155/2021/1577614 doi: 10.1155/2021/1577614
[4]	A. Glowacz, Thermographic fault diagnosis of shaft of BLDC motor, Sensors, 22 (2022), 8537. https://doi.org/10.3390/s22218537 doi: 10.3390/s22218537
[5]	H. Xu, Z. Cai, R. Li, W. Li, Efficient citycam-to-edge cooperative learning for vehicle counting in ITS, IEEE Trans. Intell. Transp. Syst., 23 (2022), 16600–16611. https://doi.org/10.1109/TITS.2022.3149657 doi: 10.1109/TITS.2022.3149657
[6]	Y. Y. Tseng, T. C. Hsu, Y. F. Wu, J. J. Chen, Y. C. Tseng, Efficient vehicle counting based on time-spatial images by neural networks, in 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), IEEE, (2021), 383–391. https://doi.org/10.1109/MASS52906.2021.00055
[7]	M. Haris, A. Glowacz, Lane line detection based on object feature distillation, Electronics, 10 (2021), 1102. https://doi.org/10.3390/electronics10091102 doi: 10.3390/electronics10091102
[8]	Z. Xie, R. Rajamani, Vehicle counting and maneuver classification with support vector machines using low-density flash lidar, IEEE Trans. Veh. Technol., 71 (2021), 86–97. https://doi.org/10.1109/TVT.2021.3125919 doi: 10.1109/TVT.2021.3125919
[9]	A. Glowacz, Ventilation diagnosis of angle grinder using thermal imaging, Sensors, 21 (2021), 2853. https://doi.org/10.3390/s21082853 doi: 10.3390/s21082853
[10]	C. Liu, D. Q. Huynh, Y. Sun, M. Reynolds, S. Atkinson, A vision-based pipeline for vehicle counting, speed estimation, and classification, IEEE Trans. Intell. Transp. Syst., 22 (2020), 7547–7560. https://doi.org/10.1109/TITS.2020.3004066
[11]	A. M. Santos, C. J. Bastos-Filho, A. Maciel, Counting vehicle by axes with high-precision in brazilian roads with deep learning methods, in International Conference on Intelligent Systems Design and Applications, Springer, Cham, 418 (2021), 188–198. https://doi.org/10.1007/978-3-030-96308-8_17
[12]	A. Glowacz, Thermographic fault diagnosis of ventilation in BLDC motors, Sensors, 21 (2021), 7245. https://doi.org/10.3390/s21217245 doi: 10.3390/s21217245
[13]	O. E. A. Agudelo, C. E. M. Marín, R. G. Crespo, Sound measurement and automatic vehicle classification and counting applied to road traffic noise characterization, Soft Comput., 25 (2021), 12075–12087. https://doi.org/10.1007/s00500-021-05766-6 doi: 10.1007/s00500-021-05766-6
[14]	A. Alsanabani, A. Ahmed, A. M. Al Smadi, Vehicle counting using detecting-tracking combinations: A comparative analysis, in 2020 The 4th International Conference on Video and Image Processing, (2020), 48–54. https://doi.org/10.1145/3447450.3447458
[15]	H. Lin, Z. Yuan, B. He, X. Kuai, X. Li, R. Guo, A deep learning framework for video-based vehicle counting, Front. Phys., 10 (2022), 32. https://doi.org/10.3389/fphy.2022.829734 doi: 10.3389/fphy.2022.829734
[16]	M. Fachrie, A simple vehicle counting system using deep learning with YOLOv3 model, Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4 (2020), 462–468. https://doi.org/10.29207/resti.v4i3.1871 doi: 10.29207/resti.v4i3.1871
[17]	K. Yin, L. Wang, J. Zhang, ST-CSNN: a novel method for vehicle counting, Mach. Vision Appl., 32 (2021), 1–13. https://doi.org/10.1007/s00138-021-01233-2 doi: 10.1007/s00138-021-01233-2
[18]	Y. Youssef, M. Elshenawy, Automatic vehicle counting and tracking in aerial video feeds using cascade region-based convolutional neural networks and feature pyramid networks, Trans. Res. Rec., 2675 (2021), 304–317. https://doi.org/10.1177/0361198121997833 doi: 10.1177/0361198121997833
[19]	J. Navarro, D. S. Benítez, N. Pérez, D. Riofrío, R. F. Moyano, Towards a low-cost embedded vehicle counting system based on deep-learning for traffic management applications, in 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2021, 1–6. https://doi.org/10.1109/CHILECON54041.2021.9702914
[20]	S. Djukanović, Y. Patel, J. Matas, T. Virtanen, Neural network-based acoustic vehicle counting, in 2021 29th European Signal Processing Conference (EUSIPCO), 2021,561–565. https://doi.org/10.23919/EUSIPCO54536.2021.9615925
[21]	Z. Al-Ariny, M. A. Abdelwahab, M. Fakhry, E. S. Hasaneen, An efficient vehicle counting method using mask r-cnn, in 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), 2020,232–237. https://doi.org/10.1109/ITCE48509.2020.9047800
[22]	J. Liu, R. Jia, W. Li, F. Ma, H. M. Abdullah, H. Ma, et al., High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines, Energy Rep., 6 (2020), 2430–2440. https://doi.org/10.1016/j.egyr.2020.09.002 doi: 10.1016/j.egyr.2020.09.002
[23]	M. Ahmad, M. Abdullah, D. Han, Small object detection in aerial imagery using RetinaNet with anchor optimization, in 2020 International Conference on Electronics, Information, and Communication (ICEIC), 2020, 1–3. https://doi.org/10.1109/ICEIC49074.2020.9051269
[24]	G. Dhiman, V. Kumar, Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Softw., 114 (2017), 48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014 doi: 10.1016/j.advengsoft.2017.05.014
[25]	A. Saha, P. Dash, N. R. Babu, T. Chiranjeevi, M. Dhananjaya, L. Knypiński, Dynamic stability evaluation of an integrated biodiesel-geothermal power plant-based power system with spotted hyena optimized cascade controller, Sustainability, 14 (2022), 14842. https://doi.org/10.3390/su142214842 doi: 10.3390/su142214842
[26]	M. Gafar, R. A. El-Sehiemy, H. M. Hasanien, A. Abaza, Optimal parameter estimation of three solar cell models using modified spotted hyena optimization, J. Ambient Intell. Human. Comput., 2022 (2022), 1–12. https://doi.org/10.1007/s12652-022-03896-9 doi: 10.1007/s12652-022-03896-9
[27]	H. D. Shao, H. K. Jiang, X. Q. Li, S. P. Wu, Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine, Knowl. Based Syst., 140 (2018), 1–14. https://doi.org/10.1016/j.knosys.2017.10.024 doi: 10.1016/j.knosys.2017.10.024
[28]	I. Abd El Kader, G. Xu, Z. Shuai, S. Saminu, I. Javaid, I. S. Ahmad, et al., Brain tumor detection and classification on MR images by a deep wavelet auto-encoder model, Diagnostics, 11 (2021), 1589. https://doi.org/10.3390/diagnostics11091589 doi: 10.3390/diagnostics11091589
[29]	H. D. Shao, H. K. Jiang, K. Zhao, D. D. Wei, Dongdong, X. Q. Li, A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings, Mech. Syst. Signal Process., 110 (2018), 193–209. https://doi.org/10.1016/j.ymssp.2018.03.011 doi: 10.1016/j.ymssp.2018.03.011
[30]	H. Song, H. Liang, H. Li, Z. Dai, X. Yun, Vision-based vehicle detection and counting system using deep learning in highway scenes, Eur. Transp. Res. Rev., 11 (2019), 1–16. https://doi.org/10.1186/s12544-019-0390-4 doi: 10.1186/s12544-019-0390-4

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