Have a personal or library account? Click to login
IoT-Based Emergency Vehicle Detection Using YOLOv8 Cover

References

  1. A. Baghel, A. Srivastava, A. Tyagi, S. Goel, and P. Nagrath, “Analysis Of Ex-Yolo Algorithm With Other Real-Time Algorithms For Emergency Vehicle Detection,” in Intelligent Methods in Emerging Robotics and Automation, vol. 978, Lecture Notes in Networks and Systems, Springer, 2020, doi: 10.1007/978-981-15-3369-3_45.
  2. V. T. Tran and W. H. Tsai, “Audio-Vision Emergency Vehicle Detection,” IEEE Sensors Journal, vol. 21, no. 24, pp. 27905–27917, Dec. 2021, doi: 10.1109/JSEN.2021.3127893.
  3. N. Dahiya, M. Garg, S. Gupta, and D. Gupta, “Smart Ambulance System Using Internet of Things: A Rumination,” Journal of Computational and Theoretical Nanoscience, vol. 16, pp. 4249–4254, 2019, doi: 10.1166/jctn.2019.8508.
  4. W.-J. Chang, L.-B. Chen, and K.-Y. Su, “DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification,” IEEE Access, vol. 7, pp. 148 163–148 175, 2019, doi: 10.1109/ACCESS.2019.2946468.
  5. A. Benjumea, I. Teeti, F. Cuzzolin, and A. Bradley, “YOLO-Z: Improving Small Object Detection in YOLOv5 for Autonomous Vehicles,” arXiv preprint arXiv:2112.11798, 2021.
  6. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, June 2017, doi: 10.1109/TPAMI.2016.2577031.
  7. M. Zuraimi and F. Zaman, “Vehicle Detection and Tracking Using YOLO and DeepSORT,” in Proc. 2021 Int. Seminar on Intelligent Control and Future Energy (ISICFE), pp. 23–29, 2021, doi: 10.1109/ISCAIE51753.2021.9431784.
  8. J. Lu, C. Ma, L. Li, X. Xing, Y. Zhang, Z. Wang, and J. Xu, “A Vehicle Detection Method for Aerial Image Based on YOLO,” Journal of Computer and Communications, vol. 6, no. 11, pp. 98–107, Nov. 2018, doi: 10.4236/jcc.2018.611009.
  9. J. Hussain, B. R. P. Prathap, and A. Sharma, “An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming,” in Data Science and Security: Proceedings of IDSCS 2022, Singapore: Springer Nature Singapore, 2022, pp. 305–316, doi: 10.1007/978-981-19-2211-4_27.
  10. K. Munasinghe, T. Waththegedara, I. Wickramas-inghe, O. K. Herath, and L. Velmanickam, “Smart Traffic Light Control System Based on Traffic Density and Emergency Vehicle Detection,” in Proc. 2022 Middle East Conf. on Innovation in Revision and Communication (MERCon), pp. 1–6, 2022, doi: 10.1109/MERCon55799.2022.99061 84.
  11. A. Ashir, “A Transfer-Learning-Based Approach for Emergency Vehicle Detection,” Eurasian Journal of Science and Engineering, vol. 8, no. 1, p. 75, 2022, doi: 10.23918/eajse.v8i1p75.
  12. R. Huang, J. Pedoeem, and C. Chen, “YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers,” in Proc. 2018 IEEE International Conference on Big Data (Big Data), pp. 2503–2507, 2018.
  13. W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments,” IEEE Access, vol. 8, pp. 1935–1944, 2020, doi: 10.1109/ACCESS.2 019.2961959.
  14. L. Cheng, J. Li, P. Duan, and M. Wang, “A Small Attentional YOLO Model for Landslide Detection from Satellite Remote Sensing Images,” Landslides, vol. 18, no. 10, pp. 3251–3264, 2021, doi: 10.1007/s10346-021-01694-6.
  15. C. Dewi, R.-C. Chen, Y.-T. Liu, X. Jiang, and K. Hartomo, “YOLO V4 for advanced traffic sign recognition with synthetic training data gener-ated by various GAN,” IEEE Access, vol. 9, pp. 97236–97248, 2021, doi: 10.1109/ACCESS.202 1.3094201.
  16. S. Chen and W. Lin, “Embedded System Real-Time Vehicle Detection Based on Improved YOLO Network,” in Proceedings of the IEEE 2nd International Conference on Intelligent Control, Measurement and Computing (ICICMC), pp. 1400–1403, 2019, doi: 10.1109/IMCEC46724.2019.8984 055.
DOI: https://doi.org/10.14313/jamris-2025-018 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 79 - 88
Submitted on: May 24, 2023
Accepted on: Jul 17, 2023
Published on: Jun 26, 2025
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 Syed Suhana, Boppuru Rudra Prathap, Kavish Narang, Ivin Anto, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.