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A dam image crack detection method based on transfer learning

A technology of transfer learning and detection methods, which is applied in image enhancement, image analysis, image data processing, etc., to reduce the amount of calculation and calculation time, solve the problem of over-fitting, and reduce the effect of redundant expression

Active Publication Date: 2019-02-15
HOHAI UNIV
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Problems solved by technology

[0004] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a dam image crack detection method based on transfer learning, which solves the over-fitting problem in the case of small data sets, and improves the prediction performance and operation through the idea of ​​transfer learning. speed

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  • A dam image crack detection method based on transfer learning
  • A dam image crack detection method based on transfer learning
  • A dam image crack detection method based on transfer learning

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[0036] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0037] Such as figure 1 As shown, a method for detecting cracks in dam images based on transfer learning proposed by the present invention, the specific steps are as follows.

[0038] Step 1. Collect images of dam cracks. In the application, in order to reduce the impact of over-fitting due to insufficient data sets, the image data set is preprocessed by GAN to fill the data set. GAN includes a generator G and a discriminator D. The generator receives a random noise z, and generates a new sample picture through this noise, which is denoted as G(z); the discriminator distinguishes the authenticity of the received picture, and the output is the received The probability that the picture is a real picture, if the input is a real sample, the closer the output is to 1, and the closer the output is to 0 if the input is a fake sample. In the actu...

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Abstract

The invention discloses a dam image crack detection method based on transfer learning. The method comprises the following steps of collecting the dam crack image, preprocessing the image data set to fill the data set through a generated antagonism network GAN; using a pre-training model MobileNet that does not retain a top layer and a full-connection layer to extract image features, splicing a Flatten layer after MobileNet, splicing the full connection layer whose activation function is ReLU after the Flatten layer, and splicing the Full Connection Layer whose activation function is Sigmoid asthe output layer; frozening the first K depth decomposable convolution structures in MobileNet, and fixing the relative weights of these K depth decomposable convolution structures; training the model and only updating the weights of the unfrozen network layer in the process of model training; using the trained model to detect the dam cracks in the image. The method of the invention solves the over-fitting problem under the condition of small data set, and improves the prediction performance and the running speed through the migration learning idea.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a method for detecting cracks in dam images based on migration learning. Background technique [0002] Our country has achieved world-renowned achievements in the development of water conservancy construction. As an important part of water conservancy projects, how to carry out effective safety diagnosis of dams has been a constant discussion in the academic circles. Due to its basic nature, the dam has been subjected to temperature gradients, high water pressure, water scour, seepage, erosion, etc., which will inevitably produce cracks. The cracks in the dam body may form concentrated leakage channels, deteriorating the dam The operating state of the dam affects the safety of the dam body, so the detection of dam cracks is extremely important. [0003] At present, there are many methods for analyzing dam monitoring data, such as multiple linear regression,...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T2207/30132
Inventor 刘凡杨丽洁毛莺池许峰辛仰鑫
Owner HOHAI UNIV
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