A Novel Framework for Vehicle Detection and Classification Using Enhanced YOLO-v7 and GBM to Prioritize Emergency Vehicle
Keywords:
Emergency Vehicles, GBM, Shadow Detection, Vehicle Detection, YOLO-v7Abstract
In the modern era, accurate vehicle detection and classification has become one of the key challenges due to the rapid increment in the number of varied size vehicles over the roads, particularly in urban regions. There have been explored and implemented promiscuous models for vehicle detection and categorization in the last few years. However, existing advanced frameworks have several issues related to the correct identification of the vehicles due to the shadow problem, which results in lower identification accuracy for prioritization of emergency vehicles. Further, these models consume more time in execution as well as intricate to implement and maintain in real-time. In this research work, a new framework for vehicle detection and classification has been proposed. This novel framework is based on Yolo-v7 and a Gradient boosting machine (GBM) to prioritize emergency vehicles in a faster and more accurate manner. For alleviating huge traffic as well as safety concerns, this suggested framework centers on the accurate identification of vehicles class in intelligent transportation system (ITS) for assigning priority to emergency vehicles to get a clear path. The findings of the suggested framework are highly optimal as well as enhanced in comparison to the previous models. The evaluated performance metrics i.e. accuracy, precision, recall, and F1-score of the suggested enhanced Yolo-v7 and GBM-based model are 98.83%, 96%, 97% and 98%respectively. This proposed research work can be extended in the future for more accurate vehicle identification to handle multifarious challenging circumstances, namely snow and rainy conditions or during the night.
Downloads
References
F. Huang, S. Chen, Q. Wang, Y. Chen, and D. Zhang, “Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study,” Int. J. Digit. Earth, 2023, doi: 10.1080/17538947.2023.2187465.
L. Qiu, D. Zhang, Y. Tian, and N. Al-Nabhan, “Deep learning-based algorithm for vehicle detection in intelligent transportation systems,” J. Supercomput., 2021, doi: 10.1007/s11227-021-03712-9.
C. Chen, B. Liu, S. Wan, P. Qiao, and Q. Pei, “An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System,” IEEE Trans. Intell. Transp. Syst., 2021, doi: 10.1109/TITS.2020.3025687.
B. Mahaur, N. Singh, and K. K. Mishra, “Road object detection: a comparative study of deep learning-based algorithms,” Multimed. Tools Appl., 2022, doi: 10.1007/s11042-022-12447-5.
M. Oplenskedal, P. Herrmann, and A. Taherkordi, “DEEPMATCH2: A comprehensive deep learning-based approach for in-vehicle presence detection,” Inf. Syst., 2022, doi: 10.1016/j.is.2021.101927.
S. Vikruthi, M. Archana, and R. C. Tanguturi, “ENHANCED VEHICLE DETECTION USING POOLING BASED DENSE-YOLO MODEL,” J. Theor. Appl. Inf. Technol., 2022.
S. Vikruthi, M. Archana, and R. C. Tanguturi, “Shadow Detection and Elimination Technique for Vehicle Detection,” Rev. d’Intelligence Artif., 2022, doi: 10.18280/ria.360513.
S. Usmankhujaev, S. Baydadaev, and K. J. Woo, “Real-time, deep learning basedwrong direction detection,” Appl. Sci., 2020, doi: 10.3390/app10072453.
S. Karungaru, L. Dongyang, and K. Terada, “Vehicle Detection and Type Classification Based on CNN-SVM,” Int. J. Mach. Learn. Comput., 2021, doi: 10.18178/ijmlc.2021.11.4.1052.
X. Kong, J. Zhang, L. Deng, and Y. K. Liu, “Research Advances on Vehicle Parameter Identification Based on Machine Vision,” Zhongguo Gonglu Xuebao/China Journal of Highway and Transport. 2021. doi: 10.19721/j.cnki.1001-7372.2021.04.002.
M. T. Mahmood, S. R. A. Ahmed, and M. R. A. Ahmed, “Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism,” 2020. doi: 10.1088/1757-899X/928/2/022027.
L. Nie, Z. Ning, X. Wang, X. Hu, J. Cheng, and Y. Li, “Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method,” IEEE Trans. Netw. Sci. Eng., 2020, doi: 10.1109/TNSE.2020.2990984.
Y. Cai, Z. Liu, H. Wang, X. Chen, and L. Chen, “Vehicle detection by fusing part model learning and semantic scene information for complex urban surveillance,” Sensors (Switzerland), 2018, doi: 10.3390/s18103505.
V. Keerthi Kiran, P. Parida, and S. Dash, “Vehicle detection and classification: A review,” 2021. doi: 10.1007/978-3-030-49339-4_6.
W. Omar, Y. Oh, J. Chung, and I. Lee, “Aerial dataset integration for vehicle detection based on YOLOv4,” Korean J. Remote Sens., 2021, doi: 10.7780/kjrs.2021.37.4.6.
[16] N. Singhal and L. Prasad, “Sensor based vehicle detection and classification - a systematic review,” Int. J. Eng. Syst. Model. Simul., 2022, doi: 10.1504/IJESMS.2022.122731.
J. Trivedi, M. S. Devi, and D. Dhara, “Vehicle classification using the convolution neural network approach,” Sci. J. Silesian Univ. Technol. Ser. Transp., 2021, doi: 10.20858/sjsutst.2021.112.16.
S. Sathruhan, O. K. Herath, T. Sivakumar, and A. Thibbotuwawa, “Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach,” 2022. doi: 10.1109/SLAAI-ICAI56923.2022.10002605.
E. Akdag, E. Bondarev, and P. H. N. De With, “Critical Vehicle Detection for Intelligent Transportation Systems,” 2022. doi: 10.5220/0010968900003191.
T. Sarapirom and S. Poochaya, “Detection and classification of incoming ambulance vehicle using artificial intelligence technology,” 2021. doi: 10.1109/ECTI-CON51831.2021.9454821.
Y. A. Al Khafaji and N. K. El Abbadi, “Traffic Signs Detection and Recognition Using A combination of YOLO and CNN,” 2022. doi: 10.1109/IICCIT55816.2022.10010598.
P. Rosayyan, J. Paul, S. Subramaniam, and S. I. Ganesan, “An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors,” Meas. Sensors, 2023, doi: 10.1016/j.measen.2023.100697.
H. Wang, X. Lou, Y. Cai, Y. Li, and L. Chen, “Real-time vehicle detection algorithm based on vision and LiDAR point cloud fusion,” J. Sensors, 2019, doi: 10.1155/2019/8473980.
A. Gomaa, T. Minematsu, M. M. Abdelwahab, M. Abo-Zahhad, and R. ichiro Taniguchi, “Faster CNN-based vehicle detection and counting strategy for fixed camera scenes,” Multimed. Tools Appl., 2022, doi: 10.1007/s11042-022-12370-9.
A. Farid, F. Hussain, K. Khan, M. Shahzad, U. Khan, and Z. Mahmood, “A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments,” Appl. Sci., 2023, doi: 10.3390/app13053059.
H. Haritha and S. K. Thangavel, “A modified deep learning architecture for vehicle detection in traffic monitoring system,” Int. J. Comput. Appl., 2021, doi: 10.1080/1206212X.2019.1662171.
R. Ma, Z. Zhang, Y. Dong, and Y. Pan, “Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck,” Sensors, vol. 20, no. 18, 2020, doi: 10.3390/s20185051.
C. Bao, J. Cao, Q. Hao, Y. Cheng, Y. Ning, and T. Zhao, “Dual-YOLO Architecture from Infrared and Visible Images for Object Detection,” Sensors, 2023, doi: 10.3390/s23062934.
Z. Qiu, H. Bai, and T. Chen, “Special Vehicle Detection from UAV Perspective via YOLO-GNS Based Deep Learning Network,” Drones, 2023, doi: 10.3390/drones7020117.
R. C. Barbosa, M. S. Ayub, R. L. Rosa, D. Z. Rodríguez, and L. Wuttisittikulkij, “Lightweight pvidnet: A priority vehicles detection network model based on deep learning for intelligent traffic lights,” Sensors (Switzerland), 2020, doi: 10.3390/s20216218.
Litty Rajan, Alpana Gopi, Divya P R, Surya Rajan. (2017). A Survey on RFID Based Vehicle Authentication Using A Smart Card. International Journal Of Computer Engineering In Research Trends, 4(3), 106-110.
Alpana Gopi, Divya P R, Litty Rajan, Surya Rajan, Shini Renjith. (2016). Accident Tracking and Visual Sharing Using RFID and SDN. International Journal of Computer Engineering In Research Trends, 3(10), 544-549.
Porag Kalita. (2017). Mechanization and Computerization in Road Transport Industry: Study for Vehicular Ledger. International Journal of Computer Engineering In Research Trends, 4(9), 373-377.
Ramana, K., Srivastava, G., Kumar, M. R., Gadekallu, T. R., Lin, J. C.-W., Alazab, M., & Iwendi, C. (2023). A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas. In IEEE Transactions on Intelligent Transportation Systems (Vol. 24, Issue 4, pp. 3922–3934). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tits.2022.3233801
Kumar, V. K. A., Kumar, M. R., Shribala, N., Singh, N., Gunjan, V. K., Siddiquee, K. N., & Arif, M. (2022). Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks. In N. Jan (Ed.), Journal of Mathematics (Vol. 2022, pp. 1–10). Hindawi Limited. https://doi.org/10.1155/2022/3806018
Priya, S. ., & Suganthi, P. . (2023). Enlightening Network Lifetime based on Dynamic Time Orient Energy Optimization in Wireless Sensor Network. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 149–155. https://doi.org/10.17762/ijritcc.v11i4s.6321
Ahammad, D. S. H. ., & Yathiraju, D. . (2021). Maternity Risk Prediction Using IOT Module with Wearable Sensor and Deep Learning Based Feature Extraction and Classification Technique. Research Journal of Computer Systems and Engineering, 2(1), 40:45. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/19
Kumar, A., Dhabliya, D., Agarwal, P., Aneja, N., Dadheech, P., Jamal, S. S., & Antwi, O. A. (2022). Cyber-internet security framework to conquer energy-related attacks on the internet of things with machine learning techniques. Computational Intelligence and Neuroscience, 2022 doi:10.1155/2022/8803586
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.