Crack identifying method based on deep learning convolutional neural network

Abstract

The invention proposes a crack identifying method based on deep learning convolutional neural network, and the method comprises the following specific steps: A) collecting an image building, training and verifying database; B) establishing a deep learning convolution neural network; and C) using the image database in A to train the completed deep learning neural network in B.

Description

technical field [0001] The invention relates to a method for identifying cracks in an image using a deep learning convolutional neural network. Background technique [0002] Civilian infrastructure such as bridges, dams, and skyscrapers gradually degrade over time and lose their ability to function as designed. Concrete cracks or cracks in steel structures are one of the key diseases of such facilities. Although people have increased the detection of these facilities, it is necessary to close traffic or block buildings during on-site inspections. At the same time, manual detection of large areas of structural surfaces is inefficient. , it is difficult to detect timely and accurately in the face of a large number of infrastructures. [0003] Many experts have proposed various methods based on visual technology to identify damage to detect cracks on the surface of various structures. The core of these methods is image processing technology. One of the important advantages of...

AI Technical Summary

Problems solved by technology

Although they introduced machine learning algorithms, these methods inevitably require a large number of sensors and there are many extraction errors

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