MUET logo

Enhanced Real-Time Face Detection in Challenging Environments: Leveraging YOLOv8 for Partially Occluded and Low-Light Conditions

Authors

  • Amina Shaikh

    KICT, International Islamic University, Malaysia
    Author

  • Imran Khan Keerio

    Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan
    Author

  • Mutiullah Shaikh

    The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
    Author

  • Asadullah Shah

    KICT, International Islamic University, Malaysia
    Author

DOI:

https://doi.org/10.22581/0536

Keywords:

Partially Occluded Faces, Face Detection, Deep learning, Low-light Conditions , Real-time Object Detection

Abstract

Face recognition technology is considered a crucial component of modern surveillance frameworks; however, face recognition under unfavourable conditions, including partial obstruction and low-light scenarios, is still a prominent challenge. This paper analyzes how the deep learning model YOLOv8 can be used to solve these issues. The model was tested with a customized set of photos illustrating faces with different levels of occlusion (such as masks, helmets and scarves) and lighting. The researcher has classified the data into three categories 80% of the data is used in training, 10% of the data is used in testing, and 10% of the data is used in validation. The training process was taken 150 times, with critical measures such as 93% precision, 88.7% recall, an F1-score of 0.90, and a mean average precision (mAP@50) of 0.92. The model performed well in ordinary surveillance circumstances, but its detection efficacy decreased in cases of severe occlusions and low-light surroundings, as seen by a lower mAP@50-95 score of 0.512. YOLOv8 is compared with YOLOv7, RetinaFace, and Faster R-CNN, based on performance metrics. Given these challenges, YOLOv8 provides a solution for real-time face identification in security applications. Future research will focus on enhancing the model's resilience through sophisticated pre-processing approaches, multimodal detection, and transfer learning to improve performance in increasingly complex real-world scenarios.

Downloads

References

[1] H. T. Rauf, M. I. U. Lali, S. Zahoor, S. Z. H. Shah, A. U. Rehman, and S. A. C. Bukhari, “Visual features based automated identification of fish species using deep convolutional neural networks,” Comput. Electron. Agric., vol. 167, p. 105075, 2019.

[2] S. Cui, Y. Zhou, Y. Wang, and L. Zhai, “Fish detection using deep learning,” Appl. Comput. Intell. Soft Comput., vol. 2020, no. 1, p. 3738108, 2020.

[3] K. M. Knausgård, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, and M. Goodwin, “Temperate fish detection and classification: a deep learning based approach,” Appl. Intell., vol. 52, no. 6, pp. 6988–7001, 2022.

[4] D. Xu and Y. Wu, “Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection,” Sensors, vol. 20, no. 15, p. 4276, 2020.

[5] Y. H. Lee and Y. Kim, “Comparison of CNN and YOLO for object detection,” J. Semicond. Display Technol., vol. 19, no. 1, pp. 85–92, 2020.

[6] C. Dewi, B. J. Tsai, and R. C. Chen, "Shapley additive explanations for text classification and sentiment analysis of Internet Movie Database," in Proc. Asian Conf. Intell. Inf. Database Syst., Singapore, 2022, pp. 69–80.

[7] J. Wang, Y. Yuan, and G. Yu, “Face attention network: An effective face detector for the occluded faces,” arXiv preprint arXiv:1711.07246, 2017.

[8] Z. Zhao, K. Hao, X. Ma, X. Liu, T. Zheng, J. Xu, and S. Cui, “SAI-YOLO: A lightweight network for real-time detection of driver mask-wearing specification on resource-constrained devices,” Comput. Intell. Neurosci., vol. 2021, no. 1, p. 4529107, 2021.

[9] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 779–788.

[10] Q. Liu et al., “A multitask model for real-time fish detection and segmentation based on YOLOv5,” PeerJ Comput. Sci., vol. 9, p. e1262, 2023.

[11] H. Deng, J. Zhang, L. Chen, and M. Cai, “Improved mask wearing detection algorithm for SSD,” J. Phys. Conf. Ser., vol. 1757, no. 1, p. 012140, 2021.

[12] H. Malik, A. Naeem, S. Hassan, F. Ali, R. A. Naqvi, and D. K. Yon, “Multi-classification deep neural networks for identification of fish species using camera captured images,” PLoS One, vol. 18, no. 4, p. e0284992, 2023.

[13] G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics,” 2023.

[14] M. Hussain, “YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection,” Machines, vol. 11, no. 7, p. 677, 2023.

[15] T. Wu and Y. Dong, “YOLO-SE: Improved YOLOv8 for remote sensing object detection and recognition,” Appl. Sci., vol. 13, no. 24, p. 12977, 2023.

[16] J. Zheng, H. Wu, H. Zhang, Z. Wang, and W. Xu, “Insulator-defect detection algorithm based on improved YOLOv7,” Sensors, vol. 22, no. 22, p. 8801, 2022.

[17] F. Prameswari, H. Octafiani, and T. Haryanto, “You Only Look Once (YOLOv8) for fish species detection,” in IOP Conf. Ser.: Earth Environ. Sci., vol. 1359, no. 1, p. 012023, 2024.

[18] P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge, “New data preprocessing trends based on ensemble of multiple preprocessing techniques,” TrAC Trends Anal. Chem., vol. 132, p. 116045, 2020.

[19] X. Luo and S. Pradhan, “Evaluation metrics,” in Anaphora Resolution: Algorithms, Resources, and Applications. Berlin, Heidelberg: Springer, 2016, pp. 141–163.

[20] G. Silpalatha and T. S. Jayadeva, “Accelerating fast and accurate instantaneous segmentation with YOLO-v8 for remote sensing image analysis,” Remote Sens. Appl. Soc. Environ., vol. 37, p. 101502, 2025.

[21] Z. Liu, R. R. D. Abeyrathna, R. M. Sampurno, V. M. Nakaguchi, and T. Ahamed, “Faster-YOLO-AP: A lightweight apple detection algorithm based on improved YOLOv8 with a new efficient PDWConv in orchard,” Comput. Electron. Agric., vol. 223, p. 109118, 2024.

[22] Y. Wang and J. He, “A rapid concrete crack detection method based on improved YOLOv8,” IEEE Access, 2025.

[23] M. Safaldin, N. Zaghden, and M. Mejdoub, “An improved YOLOv8 to detect moving objects,” IEEE Access, 2024.

[24] S. Pan, Z. Chang, and Z. Chang, “WD-1D-VGG19-FEA: An efficient wood defect elastic modulus predictive model,” Sensors, vol. 24, no. 17, p. 5572, 2024.

[25] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” In Advances in neural information processing systems, vol.28, 2015.

[26] J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, “Retinaface: Single-shot multi-level face localisation in the wild,” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (pp. 5203-5212), 2020.

[27] C. H. Kang, and S. Y. Kim, ‘Real-time object detection and segmentation technology: an analysis of the YOLO algorithm,” JMST Advances, vol. 5, no. 2, p. 69-76, 2023.

[28] T. Wu, and Y. Dong, “YOLO-SE: Improved YOLOv8 for remote sensing object detection and recognition,” Applied Sciences, vol. 13, no. 24, 12977, 2023.

[29] Z. Liu, R. R. D. Abeyrathna, R. M. Sampurno, V. M. Nakaguchi, and T. Ahamed, Faster-YOLO-AP: A lightweight apple detection algorithm based on improved YOLOv8 with a new efficient PDWConv in orchard. Computers and Electronics in Agriculture, 223, p. 109118, 2024.

[30] C. Shorten, and , T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning. Journal of big data, vol. 6, no. 1, p.1-48, 2019.

[31] L. Wang, Y. Chang, and K. Wang, “Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition,” Applied Sciences, vol. 14, no. 20, p. 9430, 2024.

Published

2026-01-01

Issue

Vol. 45 No. 1 (2026): January Issue

Section

Articles

License

Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Enhanced Real-Time Face Detection in Challenging Environments: Leveraging YOLOv8 for Partially Occluded and Low-Light Conditions. (2026). Mehran University Research Journal of Engineering and Technology, 45(1), 192-200. https://doi.org/10.22581/0536

Similar Articles

51-60 of 88

You may also start an advanced similarity search for this article.