Research on Mask Wearing Detection Algorithm Based on YOLOv5

Yu, Shuo; Li, Hui; Gui, Fangjun; Yang, Yanqi; Lv, Chenyang

doi:10.1109/iciba52610.2021.9688011

Cited by 2 publications

(3 citation statements)

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“…In the target detection, it is necessary to compare the detection effect between the detection box and the real box. The GIoU _ Loss used in the general network solves the problem of the ratio that cannot be optimized due to the overlap of different target boxes on the basis of IoU (Yu et al ., 2021). The calculation process of GIoU is shown in equation (1):…”

Section: Methodsmentioning

confidence: 99%

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Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Yang

Shou-song

et al. 2023

IJICC

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PurposeFire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approachThis paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.FindingsBased on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.Originality/valueAiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

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Section: Methodsmentioning

confidence: 99%

“…It is crucial for the back propagation time in deep learning networks to estimate errors. Therefore, this section improves GIoU _ Loss (Yu et al ., 2021) by introducing a better theoretical CIoU _ Loss (Gu et al ., 2022) loss function.Definition of GIoU loss…”

Section: Methodsmentioning

confidence: 99%

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Yang

Shou-song

et al. 2023

IJICC

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“…YOLO is simple to implement from the perspective of its principle. Many other algorithms can currently find targets, but they consume up too much resources [13][14][15]. It is difficult for some embedded devices to keep up with the demand.…”

Section: Yolov5mentioning

confidence: 99%

Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19

Xie¹,

Rui²,

Chang³

2022

ATAIML

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Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity. In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production. The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set. The training and test results of the suggested approach are presented visually on a tensor board. With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use. According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.

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Research on Mask Wearing Detection Algorithm Based on YOLOv5

Cited by 2 publications

References 0 publications

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19

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