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Ceiling earthquake damage identification method based on convolutional neural network

A technology of convolutional neural network and identification method, which is applied in the field of ceiling earthquake damage identification based on convolutional neural network, can solve problems such as low efficiency, and achieve the effects of improving training speed, saving manpower and material resources, and saving training time

Inactive Publication Date: 2020-06-12
HARBIN INST OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of relatively low efficiency of the existing professionals in the on-site investigation of the degree of earthquake damage, and to provide a method for identifying ceiling earthquake damage based on convolutional neural networks

Method used

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  • Ceiling earthquake damage identification method based on convolutional neural network
  • Ceiling earthquake damage identification method based on convolutional neural network
  • Ceiling earthquake damage identification method based on convolutional neural network

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specific Embodiment approach 1

[0030] Specific Embodiment 1: In this embodiment, the recognition method of the ceiling earthquake damage based on the convolutional neural network is implemented in the following steps:

[0031] Step 1: Select the target sample from the ceiling earthquake damage picture, adjust the target area of ​​the picture to be in the center, and while maintaining the aspect ratio, randomly scale the size of the target sample to 224×224, perform normalization processing, and obtain preprocessing target sample;

[0032] Step 2: Rotate and mirror the preprocessed target sample to obtain the expanded ceiling earthquake damage dataset image;

[0033] Step 3: Load the pre-trained AlexNet model as a feature extractor, extract the depth feature vector of the image of the expanded ceiling earthquake damage dataset, modify the last fully connected layer of the AlexNet model, and use the cross-entropy loss function and Adam adaptive optimization function Perform backpropagation, iteratively train...

specific Embodiment approach 2

[0038] Embodiment 2: This embodiment differs from Embodiment 1 in that in step 2, the target image is rotated by 90°, 180° and 270°.

specific Embodiment approach 3

[0039] Embodiment 3: This embodiment differs from Embodiment 1 or Embodiment 2 in that in step 2, the target image is mirrored horizontally and vertically.

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Abstract

The invention discloses a ceiling earthquake damage identification method based on a convolutional neural network, belongs to the field of computer vision, and aims to solve the problem of relativelylow efficiency of earthquake damage degree investigation by professionals on site at present. The identification method comprises the steps of 1, performing normalization processing on ceiling earthquake damage pictures; 2, performing rotation and mirroring processing on the target sample; 3, loading a pre-trained AlexNet model, modifying the last full connection layer of the model, and performingback propagation by adopting a cross entropy loss function and an Adam adaptive optimization function to obtain an initially-trained AlexNet model; 4, continuing to train the model, and adjusting thelearning rate, batchsize and the like of the neural network; and 5, evaluating the test sample by using the trained AlexNet model. According to the method, the use function of the post-earthquake building can be accurately, quickly and timely evaluated only by means of a certain hardware module and in combination with the trained model, and the model accuracy is high.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a ceiling earthquake damage recognition method based on a convolutional neural network. Background technique [0002] With the development of the economic level, the quality of urban and rural buildings in my country has been significantly improved. Under moderate earthquakes, there are very few damages to the load-bearing components of houses. Through the analysis of the earthquake damage of the frame structure buildings in the Lushan earthquake, it can be seen that a large number of ceilings fell during the earthquake. Although the damage of the ceiling did not affect the mechanical properties of the building itself, it had a huge impact on the use of the building and caused huge economic losses. , and even injured people. Therefore, the post-earthquake damage identification of non-structural components is a crucial part in the assessment of building resilience. [...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04G06K9/62
CPCG06T7/0002G06N3/084G06T2207/20084G06T2207/20081G06N3/045G06F18/24G06F18/214
Inventor 籍多发温卫平岳亚男翟长海
Owner HARBIN INST OF TECH
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