Mask wearing detection method based on YOLOv5 network

Abstract

The invention discloses a mask wearing detection method based on a YOLOv5 network. The method comprises the following steps: step 1, preprocessing an original picture by using an image enhancement algorithm and dividing a data set; step 2, sending the training set pictures into a YOLOv5 network into which an attention mechanism is introduced for iterative training, thereby effectively enhancing extraction of key point information such as a human face and a mask; 3, in order to reduce overall errors, CIOU Loss is adopted as a loss function of target frame regression; 4, after training is completed, the optimal weight model is stored and tested on a test set. The result proves that under the clamping of image enhancement and an attention mechanism, the improved YOLOv5 model realizes the efficient detection of the mask wearing, not only can the face information be successfully detected, but also the mask wearing state is correctly detected, and the corresponding confidence is given. The mask wearing detection accuracy of the model under the conditions of low visibility and weak illumination intensity can reach 92%, and the model has important practical significance for epidemic situation prevention and control and public health safety maintenance.

Description

technical field [0001] The invention relates to the field of mask wearing detection, in particular to a mask wearing detection method based on YOLOv5 network. Background technique [0002] In some specific workplaces, staff are required to wear masks in order to prevent harmful gases, droplets and viruses from entering the wearer's mouth and nose. For example, hospitals generally require medical staff to wear masks. As a commonly used medical and hygiene product, masks can reduce the risk of disease infection for mask wearers. [0003] Masks have attributes such as small targets and diversity, coupled with the diversity of scenes and the complexity of interactions between targets, making the research on multi-target tracking difficult. In addition, there are still the following difficulties in mask wearing detection: in dark conditions, due to the low light intensity and low visibility, it is difficult to accurately locate the face, and the difficulty of mask wearing detect...

AI Technical Summary

Problems solved by technology

[0005] The invention technology solves the problem: In order to solve the problem that it is difficult to accurately detect the target in the scene with low visibility and dim light, and it is difficult to accurately detect the special target group (not wearing a mask) and feed back the detection information in real time, the present invention provides a A face mask detection method based on YOLOv5, which uses an image enhancement algorithm to preprocess the image, combines channel attention and spatial attention, fully excavates key feature points such as face masks, and comprehensively considers the brightness of light in actual detection Impact on Detection Accuracy

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