Bridge crack detection method based on multi-resolution convolutional network

A convolutional network and multi-resolution technology, applied in the field of image target detection, can solve the problems of low spatial accuracy of results, low resolution of feature maps, loss of spatial structure information, etc., to solve the problem of insufficient global receptive field, improve spatial accuracy, Realize the effect of accurate detection

Pending Publication Date: 2021-02-09
SHAANXI NORMAL UNIV
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However, for bridge crack detection work, the feature map obtained by the classic segmentation model has a low r

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  • Bridge crack detection method based on multi-resolution convolutional network
  • Bridge crack detection method based on multi-resolution convolutional network
  • Bridge crack detection method based on multi-resolution convolutional network

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[0070] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention, and the detailed description is as follows.

[0071] Because the bridge crack image has the characteristics of complex background, various textures, numerous noises and irregular distribution, it is difficult for traditional detection algorithms to deal with complex types of noise, which makes the detection effect poor. The semantic segmentation algorithm in the field of deep learning has very excellent feature extraction capabilities, so the inventor has developed a bridge crack detection method based on multi-resolution convolutional networks through a large number of exp...

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Abstract

The invention belongs to the technical field of image target detection, and particularly relates to a bridge crack detection method based on a multi-resolution convolutional network, which comprises the following steps: 1, constructing a bridge crack data set; 2, labeling and classifying the training samples; 3, building an improved HRBCS bridge crack semantic segmentation model based on the HRNetmodel; 4, training the semantic segmentation model built in the step 3; 5, testing the semantic segmentation model trained in the step 4; 6, performing actual detection. Compared with the prior art,the method has the advantages that the spatial accuracy is greatly improved, a high-resolution segmentation result can be obtained, segmentation of small cracks and complex cracks is greatly improved,different types of cracks can be detected and classified, and a high-quality bridge crack segmentation mask can be generated; therefore, a corresponding maintenance scheme is formulated according tothe damage degree of different types of crack evaluation to the bridge.

Description

technical field [0001] The invention belongs to the technical field of image target detection, and in particular relates to a bridge crack detection method based on a multi-resolution convolutional network. Background technique [0002] As an important carrier connecting two large-span locations, bridges play an important role in my country's road transportation. However, during the long-term sun, rain and load operation of the bridge, the internal stress generated will also be transmitted to some weak parts along the bridge structure, resulting in the occurrence and development of cracks on the surface of the structure at this position. The damage degree of cracks to bridge structures is also different. If the extension direction of surface cracks is perpendicular to the bearing surface of the structure, it is easy to cause unsafe accidents. [0003] According to engineering practice and theoretical analysis, most bridges in service work with cracks, and the potential dama...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06T7/62G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06T7/10G06T7/62G06N3/08G06T2207/10004G06T2207/30132G06N3/045G06F18/24G06F18/214
Inventor 李良福武彪
Owner SHAANXI NORMAL UNIV
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