COP : TARGET RECKS USING YOLOv8


EOI: 10.11242/viva-tech.01.07.020

Download Full Text here



Citation

Abhishek Mandavkar, Dishant Save, Yash Patil, Prof. Janhavi Sangoi, "COP : TARGET RECKS USING YOLOv8", VIVA-IJRI Volume 1, Issue 7, Article COMP_20, pp. 1-11, 2024. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

This paper presents a comprehensive study on using YOLOv8 for target detection in various real-time applications. YOLO (You Only Look Once) has evolved as one of the most effective object detection models, and with the latest version YOLOv8, its performance has been optimized for higher accuracy and speed. The paper explores the use of YOLOv8 for detecting targets in dynamic environments, such as surveillance and autonomous systems, with a focus on real-time processing. Key challenges and advancements in object detection, particularly related to YOLO, are discussed, along with its potential applications in various industries including security, transportation, and robotics.

Keywords

YOLOv8, Target Detection, Real-Time Processing, Object Detection, Deep Learning, Surveillance, Autonomous Systems.

References

  1. Kumar, N.; Nagarathna; Flammini, F. YOLO-Based Light-Weight Deep Learning Models for Insect Detection System with Field Adaption. Agriculture 2023, 13, 741. https://doi.org/10.3390/agriculture13030741
  2. Sergejs Kodors, Marks Sondors, Gunārs Lācis, Edgars Rubauskis, Ilmārs Apeināns, Imants Zarembo, Environment. Technology. Resources. Rezekne, Latvia Proceedings of the 14th International Scientific and Practical Conference. Volume 1, 81-85, 2023
  3. Tausif Diwan, G. Anirudh, Jitendra V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications”, Department of Computer Science & Engineering, Indian Institute of Information Technology, Nagpur, India and Department of Data science and analytics, Central University of Rajasthan, Jaipur, Rajasthan, India, 2022
  4. Sita M. Yadav, Sandeep M. Chaware, “Video Object Detection through Traditional and Deep Learning Methods”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958 (Online), Volume-9 Issue-4, April, 2020
  5. Zhuo Bian, Liangliang Wang, “Detecting Moving Object via Projection of Forward-Backward Frame Difference”, International Journal of Future Generation Communication and Networking Vol. 9, No. 5 (2016), pp. 143-152
  6. Mohammed Boukabous, Mostafa Azizi, “Image and video-based crime prediction using object detection and deep learning”, Bulletin of Electrical Engineering and Informatics Vol. 12, No. 3, June 2023, pp. 1630~1638
  7. Thomas Tsaoa and Doris Y. Tsao, “A topological solution to object segmentation and tracking”, National Academy of Sciences, 2020
  8. Mingqi Gao, Feng Zheng, James J. Q. Yu, Caifeng Shan, Guiguang Ding, Jungong Han, “Deep learning for video object segmentation: a review”, Artificial Intelligence Review (2023) 56:457–531, April 2022
  9. Jyoti Kini, Fahad Shahbaz Khan, Salman Khan, Mubarak Shah, “Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging”, MBZ University of Artificial Intelligence UAE, 22 April 2022
  10. Yadang Chen, Duolin Wang, Zhiguo Chen, Zhi-Xin Yang, Enhua Wu, “Global video object segmentation with spatial constraint module”, Computational Visual Media, 2022
  11. Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen, “Future Video Synthesis with Object Motion Prediction”, arXiv:2004.00542v2 [cs.CV] 15 Apr 2020
  12. Haidi Zhu, Haoran Wei, Baoqing Li, Xiaobing Yuan, Nasser Kehtarnavaz, “A Review of Video Object Detection: Datasets, Metrics and Methods”, Appl. Sci. 2020, 10, 7834
  13. Weiguo YI, BO Wang, “Research on Underwater Small Target Detection Algorithm Based on Improved YOLOv7”, IEEE ACCESS, 28 June 2023
  14. Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, “YOLOv7-sea: Object Detection of Maritime UAV Images based on Improved YOLOv7”, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA, 2023