Abstract
In the era of smart cities and advancing transportation technologies, predicting logistic vehicle and vehicle speed is pivotal to enhancing traffic management, safety, and overall transportation efficiency. Properly predicting vehicle and vehicle speed is critical to the interests of both road users and traffic authorities. However, accurately predicting the vehicle speed and logistics vehicle of a single trip is a difficult task. In some cases, unpredicted accidents will happen, so death cases will increase. To overcome these issues, a novel Logistic Vehicle speed detection using the YOLO (LV-YOLO) method has been introduced to detect logistical vehicles and speed using the YOLO network. The proposed framework is divided into three layers such as image acquisition, segmentation layer, and detection layer. In the image acquisition layer, a CCTV camera captures highway traffic video. The collected video is converted into frames. In the segmentation layer, the video frame is segmented using U-Net, which segments the vehicle in the video frames. The detection layer performs truck detection, and speed detection using LV-YOLO on segmented frames based on the Boxy Vehicle dataset. The simulated results show that the LV-YOLO technique maintains excellent mAP levels of 99.42%. The LV-YOLO improves the overall mAP by 1.72, 5.42, and 0.82% better than the Simple Vehicle Counting System, Real-Time Detection, and Advance YOLOv3 Model for vehicle detection, 4.81, and 2.63% better than Deep Learning and CAN protocol, and 1D-CNN speed estimation mode for speed prediction respectively.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03404-w/MediaObjects/11760_2024_3404_Fig9_HTML.png)
Similar content being viewed by others
Data availability
Data sharing is not applicable to this article as no new data were created or analyzed in this Research.
References
Chen, Y., Li, Z.: An effective approach of vehicle detection using deep learning. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/2019257
Appathurai, A., Sundarasekar, R., Raja, C., Alex, E.J., Palagan, C.A., Nithya, A.: An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits Syst. Signal Process. 39, 734–756 (2020). https://doi.org/10.1007/s00034-019-01224-9
Farid, A., Hussain, F., Khan, K., Shahzad, M., Khan, U., Mahmood, Z.: A fast and accurate real-time vehicle detection method using deep learning for unconstrained environments. Appl. Sci. 13(5), 3059 (2023). https://doi.org/10.3390/app13053059
Wu, Q., Li, X., Wang, K., Bilal, H.: Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles. Soft. Comput. 27(23), 18195–18213 (2023). https://doi.org/10.1007/s00500-023-09278-3
Alhuthali, S.A.H., Zia, M.Y.I., Rashid, M.: A simplified traffic flow monitoring system using computer vision techniques. In: 2022 2nd international conference on computing and information technology (ICCIT). IEEE, 167–170 (2022). https://doi.org/10.1109/iccit52419.2022.9711550
Othmani, M.: A vehicle detection and tracking method for traffic video based on faster R-CNN. Multimed. Tools Appl. 81(20), 28347–28365 (2022). https://doi.org/10.1007/s11042-022-12715-4
Ammar, A., Koubaa, A., Boulila, W., Benjdira, B., Alhabashi, Y.: A multi-stage deep-learning-based vehicle and license plate recognition system with real-time edge inference. Sensors 23(4), 2120 (2023). https://doi.org/10.3390/s23042120
Rafique, A.A., Al-Rasheed, A., Ksibi, A., Ayadi, M., Jalal, A., Alnowaiser, K., Meshref, H., Shorfuzzaman, M., Gochoo, M., Park, J.: Smart traffic monitoring through pyramid pooling vehicle detection and filter-based tracking on aerial images. IEEE Access 11, 2993–3007 (2023). https://doi.org/10.1109/access.2023.3234281
Gu, Y., Si, B.: A novel lightweight real-time traffic sign detection integration framework based on YOLOv4. Entropy 24(4), 487 (2022). https://doi.org/10.3390/e24040487
Gayathri, K., Ajitha Gladis, K.P., Angel Mary, A.: Real time masked face recognition using deep learning based yolov4 network. Int. J. Data Sci. Artif. Intell. 01(01), 26–32 (2023). https://doi.org/10.1145/3484824.3484903
Hussain, T., Yang, B., Rahman, H.U., Iqbal, A., Ali, F.: Improving source location privacy in social internet of things using a hybrid phantom routing technique. Comput. Secur. 123, 102917 (2022). https://doi.org/10.1016/j.cose.2022.102917
Fachrie, M.: A simple vehicle counting system using deep learning with YOLOv3 model. J. RESTI (Rekayasa Sistem Dan Teknologi Informasi) 4(3), 462–468 (2020). https://doi.org/10.29207/resti.v4i3.1871
Kim, J.: Vehicle detection using deep learning technique in tunnel road environments. Symmetry 12(12), 2012 (2020). https://doi.org/10.3390/sym12122012
Sudha, D., Priyadarshini, J.: An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm. Soft. Comput. 24, 17417–17429 (2020). https://doi.org/10.1007/s00500-020-05042-z
Chen, C., Wang, C., Liu, B., He, C., Cong, L., Wan, S.: Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. (2023). https://doi.org/10.1109/tits.2022.3232153
Zaman, K., Zhaoyun, S., Shah, B., Hussain, T., Shah, S.M., Ali, F., Khan, U.S.: A novel driver emotion recognition system based on deep ensemble classification. Complex Intell. Syst. (2023). https://doi.org/10.1007/s40747-023-01338-3
Azhar, A., Rubab, S., Khan, M.M., Bangash, Y.A., Alshehri, M.D., Illahi, F., Bashir, A.K.: Detection and prediction of traffic accidents using deep learning techniques. Clust. Comput. 26(1), 477–493 (2023). https://doi.org/10.1007/s10586-021-03502-1
Karthi, S.P., RL, A.R., Buvanesh, K.K., Amalan, E. and Harishkumar, S.: Electric vehicle speed control with traffic sign detection using deep learning. In: 2022 international conference on advanced computing technologies and applications (ICACTA). IEEE, 1–6 (2022). https://doi.org/10.1109/icacta54488.2022.9753624
Jiao, X., Wang, Z., Zhang, Z.: Vehicle speed prediction using a combined neural network of convolution and gated recurrent unit with attention.
Li, Y., Wu, C., Yoshinaga, T.: Vehicle speed prediction with convolutional neural networks for ITS. In: 2020 IEEE/cic international conference on communications in China (ICCC workshops). IEEE, 41–46 (2022)
Cvijetić, A., Djukanović, S., Perunicic, A.: Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN. In: 2023 27th international conference on information technology (IT). IEEE, 1–4 (2023)
Tian, X., Zheng, Q., Yu, Z., Yang, M., Ding, Y., Elhanashi, A., Saponara, S., Kpalma, K.: A real-time vehicle speed prediction method based on a lightweight informer driven by big temporal data. Big Data Cogn. Comput. 7(3), 131 (2023)
Muthukumaran, N., Kumar, C., Joshua Samuel Raj, R., Andrew Roobert, A.: Grey wolf optimized Pi controller for high gain SEPIC converter for PV application. In: 2023 international conference on sustainable communication networks and application (ICSCNA), Theni, India, 1032–1035 (2023). https://doi.org/10.1109/ICSCNA58489.2023.10370322.
Ramaswamy, S., Joe Patrick Gnanaraj, S., Chandra Sekar, K., Muthukumaran, N.: Analysis of distribution line in link with substation using gsm technology. In: 2023 international conference on sustainable communication networks and application (ICSCNA), Theni, India, 526–528 (2023). https://doi.org/10.1109/ICSCNA58489.2023.10370197
Prabhu, M., Revathy, G., Raja Kumar, R.: Deep learning based authentication secure data storing in cloud computing. Int. J. Comput. Eng. Optim. 01(01), 10–14 (2023)
Acknowledgements
The authors would like to thank the reviewers for all of their careful, constructive and insightful comments in relation to this work.
Funding
No Financial support.
Author information
Authors and Affiliations
Contributions
The authors confirm contribution to the paper as follows: Study conception and design: Gopika rani N, Hema priya N, Ahilan A, Muthukumaran N; Data collection: Gopika rani N, Hema priya. N, Ahilan A, Muthukumaran N; Analysis and interpretation of results: Gopika rani N, Hema priya N, Ahilan A, Muthukumaran N; Draft manuscript preparation: Gopika rani, Hema priya N, Ahilan A, Muthukumaran N; All authors reviewed the results and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
This paper has no conflict of interest for publishing.
Ethical approval
My research guide reviewed and ethically approved this manuscript for publishing in this Journal.
Human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed consent
I certify that I have explained the nature and purpose of this study to the above-named individual, and I have discussed the potential benefits of this study participation. The questions the individual had about this study have been answered, and we will always be available to address future questions.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rani, N.G., Priya, N.H., Ahilan, A. et al. LV-YOLO: logistic vehicle speed detection and counting using deep learning based YOLO network. SIViP 18, 7419–7429 (2024). https://doi.org/10.1007/s11760-024-03404-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-024-03404-w