An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

Walia, Inderpreet Singh; Kumar, Deepika; Sharma, Kiran; Hemanth, D. Jude; Popescu, Daniela

doi:10.3390/electronics10232996

Cited by 34 publications

(19 citation statements)

References 44 publications

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“…The YOLOv5x model was adapted and innovated by integrating hens’ image information as a new model “ YOLOv5x-hens ” for detecting birds on the litter floor. The YOLOv5x model is one of the four most commonly used models for object detection in YOLOv5 (i.e., YOLOv5x , YOLOv5s , YOLOv5m and YOLOv5l ) [ 25 ]. Compared to the other three models, the YOLOv5x model is more powerful and flexible in detecting small-size objects such as chickens due to its enhanced characteristics of depth_multiple, width_multiple, the number of residual network (ResNet) in cross stage partial network (CSPNet), and the amount of convolution kernel (CK).…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

Yang

Chai

Bist

et al. 2022

Animals

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Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens’ behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8–16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals’ behaviors and welfare status in commercial cage-free houses.

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Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

Yang

Chai

Bist

et al. 2022

Animals

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“…Many instances report inference speed of the YOLOv5 networks at or above 60 frames per second (FPS) 38,39 for general object detection with various hardware configurations, and thus the YOLO object detector networks offer real-time image analysis. Due to their superior speed and accuracy, the YOLO networks are deployed for environmental monitoring 40 , quality control processing 41 , and checking protocol complience 42 to name but a few applications.…”

Section: A You Only Look Once (Yolo)mentioning

confidence: 99%

DropTrack -- automatic droplet tracking using deep learning for microfluidic applications

Durve,

Tiribocchi,

Bonaccorso

et al. 2022

Preprint

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“…At present, numerous emerging machine learning methods are being used more and more in object recognition. In [ 7 , 8 , 9 , 10 , 11 , 12 ], ResNet, Yolo, MobileNet, and other machine learning models are used to recognize face masks. The identification accuracy can exceed 90%.…”

Section: Introductionmentioning

confidence: 99%

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

Wang

et al. 2023

Sensors

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Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

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An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

Cited by 34 publications

References 44 publications

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

DropTrack -- automatic droplet tracking using deep learning for microfluidic applications

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

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