Roadway surface crack detection method based on improved FCN network
The invention relates to the field of crack image segmentation and semantic segmentation, and aims to solve the problems that most of the existing segmentation methods based on deep learning mainly utilize an FCN network structure or a SegNet network structure, the receptive field of the network is...
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| creator | ZHAO JUMIN REN JIAXIN JIA HUAYU |
| description | The invention relates to the field of crack image segmentation and semantic segmentation, and aims to solve the problems that most of the existing segmentation methods based on deep learning mainly utilize an FCN network structure or a SegNet network structure, the receptive field of the network is relatively small, rich context information cannot be obtained, and improvement of crack segmentation precision is affected. The invention provides a tunnel surface crack detection method based on an improved FCN network, and the method comprises the steps: taking the FCN network as a basic network model, capturing the context information in an image through combining with cavity convolution and a channel attention module, further determining the position of a crack boundary, improving the influence of noise and shielding on crack segmentation, and improving the detection precision of a tunnel surface crack. The method is advantaged in that tunnel crack segmentation accuracy is improved, tunnel surface crack automat |
| format | Patent |
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The invention provides a tunnel surface crack detection method based on an improved FCN network, and the method comprises the steps: taking the FCN network as a basic network model, capturing the context information in an image through combining with cavity convolution and a channel attention module, further determining the position of a crack boundary, improving the influence of noise and shielding on crack segmentation, and improving the detection precision of a tunnel surface crack. 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The invention provides a tunnel surface crack detection method based on an improved FCN network, and the method comprises the steps: taking the FCN network as a basic network model, capturing the context information in an image through combining with cavity convolution and a channel attention module, further determining the position of a crack boundary, improving the influence of noise and shielding on crack segmentation, and improving the detection precision of a tunnel surface crack. 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The invention provides a tunnel surface crack detection method based on an improved FCN network, and the method comprises the steps: taking the FCN network as a basic network model, capturing the context information in an image through combining with cavity convolution and a channel attention module, further determining the position of a crack boundary, improving the influence of noise and shielding on crack segmentation, and improving the detection precision of a tunnel surface crack. The method is advantaged in that tunnel crack segmentation accuracy is improved, tunnel surface crack automat</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Roadway surface crack detection method based on improved FCN network |
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