Recent Research Reviews Journal
IRO Journals
Volume 2 — Issue 1 — June 2023

A Comparative Study of Various Versions of YOLO Algorithm to Detect Drones

https://doi.org/10.36548/rrrj.2023.1.05
Gayathridevi K
Department of Information Technology (Puducherry Technological University), Puducherry
S. Kanmani
Department of Information Technology (Puducherry Technological University), Puducherry
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How to Cite

K, Gayathridevi, and S. Kanmani. 2023. “A Comparative Study of Various Versions of YOLO Algorithm to Detect Drones”. Recent Research Reviews Journal 2 (1): 54-61. https://doi.org/10.36548/rrrj.2023.1.05.
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Keywords

— YOLO
— object detection
— drone detection
— precision
— recall
Published: 31-05-2023

Abstract

Object detection algorithms with various versions of YOLO are compared with parameters like methodology, dataset used, image size, precision, recall, technology used etc. to get a conclusion as which algorithm would be the best and effective for the detection of objects. Nowadays, due to the low price and ease of use, drones can pose a malicious threat. In the field of public security and personal privacy, it is important to deploy drone detection system in restricted areas. This comparative analysis model gives a wide picture of how various object detection algorithms work, and helps in understanding the best algorithm to be used for the detection of drones with highest accuracy and precision.

References

  1. Joseph Redmon, Santosh Divvala, Yoss Girshick, Ali Farhadi, “You Only Look Once: Unified, Real-Time Object Detection” arXiv:1506.02640v1[cs.CV] 8 Jun 2015.
  2. Joseph Redmon, Ali Farhadi “YOLO9000: Better, Faster, Stronger” arXiv:1612.08242v1[cs.CV] 25 Dec 2016
  3. Hu, Yuanyuan, Xinjian Wu, Guangdi Zheng, and Xiaofei Liu. "Object detection of DRONES for anti-DRONES based on improved YOLO v3." In 2019 Chinese Control Conference (CCC), pp. 8386-8390. IEEE, 2019.
  4. Shi, Qingbang, and Jun Li. " Objects detection of DRONES for anti-DRONES based on YOLOv4." In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, pp. 1048-1052. IEEE, 2020.
  5. Singha, Subroto, and Burchan Aydin. "Automated Drone Detection Using YOLOv4." Drones 5, no. 3 (2021): 95.
  6. Maske, Shubham Rajabhau. "Micro-DRONES Detection using Mask R-CNN." PhD diss., Dublin, National College of Ireland, 2021.
  7. Al-Qubaydhi, Nader, Abdulrahman Alenezi, Turki Alanazi, Abdulrahman Senyor, Naif Alanezi, Bandar Alotaibi, Munif Alotaibi, Abdul Razaque, Abdelaziz A. Abdelhamid, and Aziz Alotaibi. "Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning." Electronics 11, no. 17 (2022): 2669.
  8. Naseri, Ahmed, and Nada Hussein M. Ali. "Detection of drones with YOLOv4 deep learning algorithm." International Journal of Nonlinear Analysis and Applications 13, no. 2 (2022): 2709-2722.
  9. Dadrass Javan, Farzaneh, Farhad Samadzadegan, Mehrnaz Gholamshahi, and Farnaz Ashatari Mahini. "A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition." Drones 6, no. 7 (2022): 160.
  10. Samadzadegan, Farhad, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini, and Mehrnaz Gholamshahi. "Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery." Aerospace 9, no. 1 (2022): 31.
  11. Alsanad, Hamid R., Amin Z. Sadik, Osman N. Ucan, Muhammad Ilyas, and Oguz Bayat. "YOLO-V3 based real-time drone detection algorithm." Multimedia Tools and Applications (2022): 1-14.
  12. Valappil, Najiya K., and Qurban A. Memon. "CNN-SVM based vehicle detection for UAV platform." International Journal of Hybrid Intelligent Systems Preprint (2021): 1-12.
  13. Bahri, Haythem, David Krčmařík, and Jan Kočí. "Accurate object detection system on hololens using yolo algorithm." In 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), pp. 219-224. IEEE, 2019.
  14. Sazdić-Jotić, Boban, Ivan Pokrajac, Jovan Bajčetić, Boban Bondžulić, and Danilo Obradović. "Single and multiple drones detection and identification using RF based deep learning algorithm." Expert Systems with Applications 187 (2022): 115928.
  15. Pham, Vung, Du Nguyen, and Christopher Donan. "Road Damages Detection and Classification with YOLOv7." arXiv preprint arXiv:2211.00091 (2022).
  16. Li, Chuyi, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke et al. "YOLOv6: a single-stage object detection framework for industrial applications." arXiv preprint arXiv:2209.02976 (2022).
  17. Ammar, Adel, Anis Koubaa, Mohanned Ahmed, Abdulrahman Saad, and Bilel Benjdira. "Vehicle detection from aerial images using deep learning: A comparative study." Electronics 10, no. 7 (2021): 820.
  18. Xin Wu, Member, Wei Li, Senior Member, Danfeng Hong,Ran Tao, and Qian Du, “Deep Learning for UAV-based Object Detection and Tracking: A Survey” arXiv:2110.12638v1[cs.CV] 25 Oct 2021
  19. John, Anand, and Divyakant Meva. "A comparative study of various object detection algorithms and performance analysis." Int J Comput Sci Eng 8 (2020): 158-163.
  20. Kurdthongmee, Wattanapong. "A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood." Heliyon 6, no. 2 (2020): e03480.
  21. Alsanad, Hamid R., Amin Z. Sadik, Osman N. Ucan, Muhammad Ilyas, and Oguz Bayat. "YOLO-V3 based real-time drone detection algorithm." Multimedia Tools and Applications (2022): 1-14. Springer
  22. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors." arXiv preprint arXiv:2207.02696 (2022).