Mask-Wearing Detection Method Based on YOLO-Mask

Chengshuo, 曹城硕 Cao; Jie, 袁杰 Yuan

doi:10.3788/lop202158.0810019

Cited by 4 publications

(2 citation statements)

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“…For one-stage frameworks, in [28] a YOLO-Mask detection algorithm has been proposed, which integrates attention mechanism into the feature extraction network to enhance the ability of representing salient features, which is designed to obtain enhanced features so as to achieve better detection performance and robustness. In [29] , the authors have proposed an improved mask-wearing detection model based on YOLOv3, where spatial pyramidal pooling (SPP) structure is adopted to promote feature fusion at different levels, and experimental results have shown the strong robustness of the proposed model in complex cases.…”

Section: Related Workmentioning

confidence: 99%

A novel attention-based enhancement framework for face mask detection in complicated scenarios

Zhang¹,

Tang²,

Wu³

et al. 2023

Signal Processing: Image Communication

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Section: Related Workmentioning

confidence: 99%

A novel attention-based enhancement framework for face mask detection in complicated scenarios

Zhang¹,

Tang²,

Wu³

et al. 2023

Signal Processing: Image Communication

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“…Finally, the average detection accuracy reaches 92.3%. Cao chengshuo [3] and others proposed a YOLO mask algorithm, which is based on YOLOv3, introduces the attention mechanism into the feature extraction network, and uses the feature pyramid and path aggregation strategy for feature fusion. The average accuracy of the algorithm is 93.33%.…”

Section: Introductionmentioning

confidence: 99%

Face Mask Wearing Detection Based on YOLOv5

Xie

Hu²,

2022

International Journal of Advanced Network, Monitoring and Controls

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In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop the spread of the epidemic and reduce cross-infection, wearing masks correctly has become an important measure to prevent the virus. Aiming at the time-consuming and laborious situation of wearing masks manually, this paper proposes a mask wearing detection method based on yolov5. The input layer is mainly used for mosaic data enhancement, that is, adaptive anchor box and adaptive image scaling technology; Yolov5 in backbone mainly adopts focus and CSP (cross stage partial) structure; The neck layer adopts spp (spatial pyramid pooling) module and FPN (feature pyramid networks) + pan (pixel aggregation network) structure; The output mainly adopts ciou for the bounding box loss function_Loss is the average index of NMS (non maximum suppression). This method uses 8000 preprocessed images as the data set and trains 200 epochs to get the final model. The algorithm visually displays the training and test results through tensor board, and inputs the pictures captured by the camera into the model to detect whether the face wears a mask. The accuracy, recall and mean accuracy (map) of the algorithm on the test set are 94.8%, 89.0% and 93.5% respectively, which are higher than the detection results of yolov3 and yolov4 algorithms.

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A novel occluded face detection approach using Enhanced ORB and optimized GAN

Nelson,

Shaji

2023

Int. J. Wavelets Multiresolut Inf. Process.

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Objectives:This research presents an approach to detect occluded face images. In order to achieve this, the research presents a novel technique that involves feature extraction and occluded face recognition. Methods: Feature extraction is performed by the enhanced ORB algorithm, which is proposed by the modification of the Oriented Fast and Rotated Brief (ORB) algorithm, by adding a phase for contrast adjustment, together with CNN features. For occluded face recognition, a Generative Adversarial Network (GAN) optimized by the proposed SR-SSA is designed. SR-SSA is proposed by the integration of Search and Rescue Optimization (SAR) in the Sparrow Search Algorithm (SSA). Results: The experimental results demonstrate that the SR-SSA-based GAN algorithm outperforms existing methods in terms of accuracy of 0.956, FAR of 0.045 and FRR of 0.021.

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Mask-Wearing Detection Method Based on YOLO-Mask

Cited by 4 publications

References 0 publications

A novel attention-based enhancement framework for face mask detection in complicated scenarios

A novel attention-based enhancement framework for face mask detection in complicated scenarios

Face Mask Wearing Detection Based on YOLOv5

A novel occluded face detection approach using Enhanced ORB and optimized GAN

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