Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network
Abstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologie...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-78746-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846147720491827200 |
|---|---|
| author | Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho |
| author_facet | Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho |
| author_sort | Binay Kumar Pandey |
| collection | DOAJ |
| description | Abstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach. |
| format | Article |
| id | doaj-art-0c40b135c4a748639cd3632392cdd65d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0c40b135c4a748639cd3632392cdd65d2024-12-01T12:27:02ZengNature PortfolioScientific Reports2045-23222024-11-0114111810.1038/s41598-024-78746-zFace mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural networkBinay Kumar Pandey0Digvijay Pandey1Mesfin Esayas Lelisho2Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology PantnagarDepartment of Technical EducationDepartment of Statistics, College of Natural and Computational Science, Mizan-Tepi UniversityAbstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach.https://doi.org/10.1038/s41598-024-78746-zFaster R-CNNIoTUAVOpenCV |
| spellingShingle | Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network Scientific Reports Faster R-CNN IoT UAV OpenCV |
| title | Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network |
| title_full | Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network |
| title_fullStr | Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network |
| title_full_unstemmed | Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network |
| title_short | Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network |
| title_sort | face mask identification with enhanced cuckoo optimization and deep learning based faster regional neural network |
| topic | Faster R-CNN IoT UAV OpenCV |
| url | https://doi.org/10.1038/s41598-024-78746-z |
| work_keys_str_mv | AT binaykumarpandey facemaskidentificationwithenhancedcuckoooptimizationanddeeplearningbasedfasterregionalneuralnetwork AT digvijaypandey facemaskidentificationwithenhancedcuckoooptimizationanddeeplearningbasedfasterregionalneuralnetwork AT mesfinesayaslelisho facemaskidentificationwithenhancedcuckoooptimizationanddeeplearningbasedfasterregionalneuralnetwork |