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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.

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References

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Published

2026-01-01

Issue

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

Section

Articles

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

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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

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