Improved Yolov8 for Detection of Intruders Using a Deep Learning Techniques

References

[1] Alam, S., Ahmed, M. U., Hasan, M., Islam, M. M., & Arnob, S. M. (2022). A comparative study of object detection models for Real Time Application in Surveillance Systems (Doctoral dissertation, Brac University).

[2] Appiah, E. O., & Mensah, S. (2023, August 22). Object detection in adverse weather condition for autonomous vehicles. Multimedia Tools and Applications, 83, 28235–28261. https://doi.org/10.1007/s11042-023-16453-z

[3] B. Bhuckory, S. Pudaruth, A multimodal IoT-based home intrusion detection system, Int. J. Comput. Theory Eng. 15 (2023) 117–124.

[4] Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021(1), 5584754.

[5] David, A. O., & Oluwasola, O. O. (2020). Zero day attack prediction with parameter setting using bi direction recurrent neural network in cyber security. International Journal of Computer Science and Information Security (IJCSIS), 18(3), 111-118.

[6] David, A. O., & Oluwasola, O. O. (2022). An optimized cloud based proxy server techniques to improve user experience of spectrum utilization. Eng. Technol. J., 7(11), 1624-1629.

[7] Fang, C., Li, C., Yang, P., Kong, S., Han, Y., Huang, X., & Niu, J. (2024). Enhancing livestock detection: An efficient model based on YOLOv8. Applied Sciences, 14(11), 4809.

[8] Geetha, D.; Monisha, S.P.; Oviya, J.; Sonia, G. Human and Animal Movement Detection in Agricultural Fields. SSRG Int. J. Comput. Sci. Eng. 2019, 6, 15–18.

[9] Kathir, M., Balaji, V., & Ashwini, K. (2024, March). Animal Intrusion Detection Using Yolo V8. In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 206-211). IEEE.

[10] Kommineni, M.; Lavanya, M.; Vardhan, V.H. Agricultural farms utilizing computer vision (ai) and machine learning techniques for animal detection and alarm systems. J. Pharm. Negat. Results 2022, 13, 3292–3300.

[11] Maktoof, M. A. J. (2023). Crowd counting using Yolov5 and KCF. Periodicals of Engineering and Natural Sciences, 11(2), 92-101.

[12] N. Thai-Nghe, H.H. Nguyen, W. Park, Q.T. Ngo, M.T. Truong, Human intrusion detection for security cameras using YOLOv8, Commun. Comput. Inf. Sci. 1950 CCIS (2024) 220–227, [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-99-766

[13] Pagano, A.; Croce, D.; Tinnirello, I.; Vitale, G. A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives. IEEE Internet Things J. 2022, 10, 3664–3679. [CrossRef]

[14] Sharma, A., Kumar, V., & Longchamps, L. (2024). Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species. Smart Agricultural Technology, 9, 100648.

[15] Shetty, A. D., & Ashwath, S. (2023, November). Animal detection and classification in image & video frames using YOLOv5 and YOLOv8. In 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 677-683). IEEE.

[16] Y. Zhang, X. Li, and W. Chen, "CNN-Based Crowd Density Estimation for Smart Surveillance," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1456-1468, Mar. 2021, doi: 10.1109/TITS.2021.3056789

[17] Yadahalli, S.; Parmar, A.; Deshpande, A. Smart intrusion detection system for crop protection by using Arduino. In Proceedings of the 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 15–17 July 2020; pp. 405–408.

@article{1710342,
      author = {Olatunji Oluwadare Oluwasola, Adeniji Oluwashola David},
      title = {Improved Yolov8 for Detection of Intruders Using a Deep Learning Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {2},
      pages = {1191-1198},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710342.pdf},
      abstract = {Intrusion detection system (IDS) is an important component of computer vision and cyber security. IDS are installed and configured to protect geographical areas from unwanted intrusion and unauthorized encroachment. conventional means of intruder detection in a farmland are quite expensive, time consuming, labor- intensive, inaccurate giving false alarm and error in their detection. In this research study, various deep learning techniques were explored to identify human intrusion within a farmland. You only look Once (YOLOV8) object detection algorithm was enhanced by adjusting the neck region and optimizing the head region in order to increase its performance. The result generated was compared to the existing YOLOV8, Regional convolution neural network and the single short detector object detection model.The improved YOLOV8 model generates a precision of 93.6%, recall value of 92.7% F-1 score of 93.8% and a mean average precision of 92.9% which shows a better model performance when compared to the other Deep Learning models. This model can be used to detect unwanted and unauthorized human intruder in a real time.},
      keywords = {CNN, Improved YOLOV8, R-CNN, SSD, Deep learning, IDS},
      month = {August},
  }