SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Nagrath, Preeti; Jain, Rachna; Madan, Agam; Arora, Rohan; Kataria, Piyush; Hemanth, D. Jude

doi:10.1016/j.scs.2020.102692

Cited by 308 publications

(143 citation statements)

References 35 publications

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“…However, the algorithm takes a lot of computational costs. Furthermore, the combination of SSD and MobileNetV2 for mask detection was proposed in paper [ 30 ], but its model structure is too complex and its performance is inferior to YOLO-v4.…”

Section: Related Workmentioning

confidence: 99%

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Zhang

2021

Sensors

191

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To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.

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Section: Related Workmentioning

confidence: 99%

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Zhang

2021

Sensors

191

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“…Face mask detection has seen significant progress in the domains of image processing and computer vision since the rise of the COVID-19 pandemic. The authors [28] used Single Shot Multibox Detector as a face detector and MobilenetV2 for the face mask detection achieved 92.64% accuracy. Another study [29] used MobileNetV2 with a global pooling block for face mask detection.…”

Section: Role Of Artificial Intelligence (Ai) In Covid-19 Pandemicmentioning

confidence: 99%

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Hussain

Ayoub

et al. 2021

Applied Sciences

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The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

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“…Vinitha et al [13] used CNN with MobileNetV2 architecture, and library of OpenCV, tensorflow, keras, and Pytorch to detect whether people were wearing a face mask or not. Nagrath et al [14] used SSDMNV2 (Single Shot Multibox Detector as a face detector and MobileNetV2) to perform real-time face masks detection. Ge et al [15] propose LLE (Locally Linear Embedding) -CNNs for face masks detection.…”

Section: Related Workmentioning

confidence: 99%

“…This study compared between KNN and SVN machine learning algorithms with CNN deep learning algorithm for face mask detection. Previous studies such as by Jagadeeswari et al [11] and Nagrath et al [14] compared only deep learning algorithm which are CNN with other architectures. This study also examines the execution time that can be useful for researchers as one of the consideration criteria to choose the algorithm.…”

Section: Performance Comparisonmentioning

confidence: 99%

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Comparative Analysis of Image Classification Algorithms for Face Mask Detection

Naufal

Kusuma

Prayuska

et al. 2021

JISEBI

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Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.

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SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Cited by 308 publications

References 35 publications

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Comparative Analysis of Image Classification Algorithms for Face Mask Detection

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