A Face-Mask Detection Approach based on YOLO V3 applied for a New Collected Dataset

In the wake of the COVID-19 pandemic, the necessity of face-mask detection systems has become paramount to enforce safety measures and regulations. This paper presents a novel approach for face-mask detection utilizing the YOLOv3 architecture applied to a newly collected dataset. The proposed system aims to accurately detect the presence or absence of face masks in real-time scenarios. The dataset used in this research is meticulously curated to encompass diverse environmental conditions, facial expressions, and variations in mask types and orientations. Each image in the dataset is annotated with bounding boxes indicating the regions of faces and masks, facilitating supervised learning for the YOLOv3 model. Our methodology involves fine-tuning the pre-trained YOLOv3 model on the collected dataset to specialize in face-mask detection. The model is trained using a combination of techniques such as data augmentation, transfer learning, and hyperparameter optimization to enhance its performance. Furthermore, to address challenges such as occlusions, varying lighting conditions, and diverse facial orientations, we incorporate techniques like multi-scale training and post-processing algorithms. These techniques aid in improving the robustness and generalization capability of the model, making it suitable for deployment in real-world scenarios.