Robust Detection of Partially Occluded Faces in Low-Light Scenarios Using YOLOv7 and YOLOv6

Partial occlusion and low light are significant challenges for face detection, limiting its effectiveness in critical applications such as security, surveillance, and user identification within computer vision. This study evaluates the effectiveness of two influential deep-learning models, YOLOv6 and YOLOv7, in identifying partially occluded faces in uncontrollable, real-world conditions. Training both models and assessing them with the help of comprehensive data-augmentation schemes that facilitate the occurrence of generalization, a carefully selected sample of partially blocked and hidden images of faces was used in all the experiments performed under low-light exposure. Findings indicate that YOLOv7 systematically outsmarts YOLOv6 in all key measures of performance, including precision (0.92 vs. 0.90), recall (0.89 vs. 0.79), as well as the mean Average Precision (mAP), which proves its ability to recognize hidden faces under adverse environments better. YOLOv7 takes a longer time to be trained, but with its enhanced design, especially the Extended Efficient Layer Aggregation Network (E-ELAN), feature extraction and real-time detection become much smoother. The statistics of this research indicate a visible increase, which indicates that YOLOv7 is reasonably suitable to be implemented in real-life, where a strong ability of face recognition, even with occlusions and low visibility, is required. This study contributes to the advancement of face detection technologies, tackling developing privacy and security needs in increasingly masked and low-visibility spaces.

@article{Alqahtani2025,
  title     = {Robust Detection of Partially Occluded Faces in Low-Light Scenarios Using YOLOv7 and YOLOv6},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nayef Alqahtani and Amina Shaikh and Imran Khan Keerio},
  doi       = {10.14569/IJACSA.2025.0161138},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161138}
}