Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection
By Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa. Sustainable cities and society, 2020
412 citations recorded by the source corpus.
Highlights • A novel deep learning model for medical face mask detection.• The model can help governments to prevent the COVID-19 transmission.• Two medical face mask datasets have been tested.• The YOLO-v2 with ResNet-50 model achieves high average precision.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/f1617f24b0ce715af13b98ba4e46e5b2d67302a5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Grandhi Sai Anirudh
- Jitendra V. Tembhurne
- S. Mirjalili
- Nour Eldeen M. Khalifa
- Tausif Diwan
- Mohamed Loey
In Radiology, Nuclear Medicine and Imaging
- S. Mirjalili
- Nour Eldeen M. Khalifa
- Mohamed Loey
- Grandhi Sai Anirudh
- Jitendra V. Tembhurne
- Tausif Diwan
Citations in the corpus
- A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, methodology): “The scarcity of COVID-19 datasets, especially in chest X-ray and CT photos, is the primary motivation for some scientific researches such as (Loey et al. Apr. 2020a; Loey et al. 2020a).”
- Object detection using YOLO: challenges, architectural successors, datasets and applications (2022, methodology): “[46] YOLO v2 Medical face mask detection • ResNet-50 as transfer learning model YOLO v2 adopted for face mask detection • Average precision 81% [84] YOLO v2 Image and color recognition • Target recognition using YOLO v2 for identifying and locating traffic light • HSV applied to transform in color s”