Mask wearing detection method fusing 3D attention mechanism and cavity convolution
The invention discloses a mask wearing detection method fusing a 3D attention mechanism and cavity convolution, relates to the technical field of machine vision target detection, solves the problem that a detection method which is high in training speed, high in detection precision and high in detec...
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Main Authors | , , , , , |
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Format | Patent |
Language | Chinese English |
Published |
23.06.2023
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Abstract | The invention discloses a mask wearing detection method fusing a 3D attention mechanism and cavity convolution, relates to the technical field of machine vision target detection, solves the problem that a detection method which is high in training speed, high in detection precision and high in detection speed needs to be provided in the prior art, and comprises the steps: marking whether mask wearing is standard or not for a first data set; an improved YOLOv5 deep learning network model is established; pre-training the improved YOLOv5 deep learning network model by using a second data set to obtain an optimal pre-training model weight; using a transfer learning mode, using the optimal pre-training model weight to initialize parameters of the improved YOLOv5 deep learning network model, using the first data set to train the model, and obtaining a mask wearing detection model. According to the method, the two indexes of detection precision and detection speed are balanced, the detection precision of the model i |
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AbstractList | The invention discloses a mask wearing detection method fusing a 3D attention mechanism and cavity convolution, relates to the technical field of machine vision target detection, solves the problem that a detection method which is high in training speed, high in detection precision and high in detection speed needs to be provided in the prior art, and comprises the steps: marking whether mask wearing is standard or not for a first data set; an improved YOLOv5 deep learning network model is established; pre-training the improved YOLOv5 deep learning network model by using a second data set to obtain an optimal pre-training model weight; using a transfer learning mode, using the optimal pre-training model weight to initialize parameters of the improved YOLOv5 deep learning network model, using the first data set to train the model, and obtaining a mask wearing detection model. According to the method, the two indexes of detection precision and detection speed are balanced, the detection precision of the model i |
Author | YAN YU LIU HAOBO ZHANG JINTONG LIU ZHE TIAN CHENGJUN WANG YUYU |
Author_xml | – fullname: TIAN CHENGJUN – fullname: LIU HAOBO – fullname: LIU ZHE – fullname: WANG YUYU – fullname: ZHANG JINTONG – fullname: YAN YU |
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DocumentTitleAlternate | 一种融合3D注意力机制和空洞卷积的口罩佩戴检测方法 |
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Snippet | The invention discloses a mask wearing detection method fusing a 3D attention mechanism and cavity convolution, relates to the technical field of machine... |
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Title | Mask wearing detection method fusing 3D attention mechanism and cavity convolution |
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