PERFORMANCE EVALUATION FOR FACE MASK DETECTION BASED ON MULT MODIFICATION OF YOLOV8 ARCHITECTURE
Muna AL-Shamdeen
muna.jaffer@uomosul.edu.iqUniversity of Mosul, College of Computer Science and Mathematics (Iraq)
https://orcid.org/0000-0002-2806-532X
Fawziya Mahmood Ramo
University of Mosul, College of Computer Science and Mathematics (Iraq)
Abstract
This work aims to engineer a robust system capable of real-time detection, accurately discerning individuals who are either adhering to or neglecting face mask mandates, across a diverse range of scenarios encompassing images, videos, and live camera streams. This study improved the architecture of YOLOv8n for face mask detection by building a new two-modification version of YOLOv8n model to improve feature extraction and prediction network for YOLOv8n. In proposed YOLOv8n-v1, the integration of a residual Network backbone into the YOLOv8n architecture by replacing the first two layers of YOLOv8n with ResNet_Stem and ResNet_Block modules to improve the model’s ability for feature extraction and replace Spatial Pyramid Pooling Fast (SPPF) module with Spatial Pyramid Pooling-Cross Stage Partial (SPPCSP) modules which combine SPP and CSP to create a network that is both effective and efficient. The proposed YOLOv8n-v2 is built by integration Ghostconv and ResNet_Downsampling modules into the proposed YOLOv8n-v1 backbone. All models have been tested and evaluated on two datasets. The first one is MJFR dataset, which contains 23,621 images, and collected by the authors of this paper from four distinct datasets, all of which were used for facemask detection purposes. The second one is MSFM object detection dataset has been collected from groups of videos in real life and images based on the curriculum learning technology. The model’s performance is assessed by using the following metrics: mean average precision (mAP50), mAP50-95, recall (R) and precision (P). It has been concluded that both versions of proposed YOLOv8n outperform the original model in terms of accuracy for both datasets. Finally, the system was successfully implemented in one of the medical clinics affiliated with a medical complex, where the results of its application showed high efficiency in various aspects of work, and it effectively contributed to improving the public health and safety.
Keywords:
YOLOv8, object detection, detection algorithm, residual networkReferences
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Authors
Muna AL-Shamdeenmuna.jaffer@uomosul.edu.iq
University of Mosul, College of Computer Science and Mathematics Iraq
https://orcid.org/0000-0002-2806-532X
Authors
Fawziya Mahmood RamoUniversity of Mosul, College of Computer Science and Mathematics Iraq
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