Crack automatic delineation method based on multi-scale feature fusion deep learning

Abstract

The invention discloses a crack automatic delineation method based on multi-scale feature fusion deep learning. The method comprises the following steps: using a migration learning strategy to obtaina crack multi-scale feature map; using a convolution layer with the size of the convolution kernel of 1x1 and a bilinear interpolation to carry out mergence layer by layer on the feature map of multiple scales and finally obtaining a multi-dimensional fusion feature; using a continuous multi-scale fully convolutional network to progressively fuse the pixel information of the multi-dimensional fusion feature to realize prediction of each pixel category in the image. According to the crack automatic delineation method based on multi-scale feature fusion deep learning in the invention, crack features of multiple scales can be learned, the relationship between pixels corresponding to different scale features and the relationship between pixels within a coverage area are fully considered, fastand high-precision crack automatic delineation is realized, and crack detection in various types of complex environments can be adapted to.

Description

technical field [0001] The invention relates to the field of structure detection and evaluation, in particular to a method for automatic detection of structural surface cracks by using images, video screens and the like. Background technique [0002] Structural cracks are one of the most common diseases in the field of civil engineering, which can cause great harm to the durability and safety of structures. Therefore, cracks are one of the main evaluation indicators for the health of various types of structures. At present, the detection of cracks is still dominated by manual detection, which requires manual marking to outline the cracks, and then analyzes the length, width, and type of cracks. This detection method requires the use of scaffolding and other equipment, which is labor-intensive, low in safety, and low in detection efficiency. [0003] Although the crack detection technology based on digital image processing has been gradually applied to structural crack detec...

AI Technical Summary

Problems solved by technology

In China, the patent document with the publication number CN106910186A discloses a bridge crack detection and positioning method based on CNN deep learning. Capable of outputting pixel-accurate fracture binarized images

The patent document with publication number CN107133960A discloses an image crack segmentation method based on fully convolutional neural network (Fully Convolutional Networks, FCN), which has the disadvantage of being insensitive to the details of cracks, and does not fully consider the relationship between pixels and pixels. , ignoring the spatial regularization step used in the usual pixel classification based segmentation methods

[0005] In general, crack status is one of the main indicators for evaluating the health status of various types of structures. Manually drawing cracks is time-consuming and laborious. The traditional crack detection technology based on digital image processing has poor applicability and is difficult to apply in complex engineering environments. The crack detection of learning has achieved certain results, but there is still a lot of room for improvement in practical engineering applications

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