Bridge crack detection data set pre-screening and calibration method

A technology of detection data and calibration method, which is applied in the field of machine learning, can solve problems such as reducing detection costs, achieve the effects of small subjective influence, make up for insufficient data volume, and save workload

Pending Publication Date: 2022-03-22
NORTHWESTERN POLYTECHNICAL UNIV
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  • Application Information

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Problems solved by technology

The invention can save manpower and calculation time, and reduce the detection cost of solving construction problems such as corresponding crack detection

Method used

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  • Bridge crack detection data set pre-screening and calibration method
  • Bridge crack detection data set pre-screening and calibration method
  • Bridge crack detection data set pre-screening and calibration method

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Embodiment Construction

[0049] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] The present invention aims to propose a method based on crack edge detection, which utilizes its characteristic of not requiring prior data to perform pre-screening and calibration of data sets, so as to achieve the purpose of reducing the workload of manual labeling and reducing the influence of subjectivity.

[0051] Such as figure 1 As shown, a bridge crack detection data set pre-screening and calibration method, including the following steps:

[0052] Step 1: Obtain multiple original images of the bridge surface, bottom and piers;

[0053] Step 2: If figure 2 As shown, the obtained original image is preprocessed and coarsely screened, the specific method is as follows:

[0054] Step 2-1: Resize each original image to 640*640, and the pixel position of the image is represented by (x, y);

[0055] Step 2-2: Perform grayscale processing on ...

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Abstract

The invention discloses a bridge crack detection data set pre-screening and calibration method, and aims to reduce the workload of a complicated process of manually screening data of tens of thousands of images and labeling images meeting requirements, and effectively reduce the subjective factor influence of a screener in the screening process. Firstly, a bridge image is acquired, edge detection and threshold segmentation based on a traditional algorithm are performed on the acquired bridge image, contour features are extracted, and a non-crack image is preliminarily judged and abandoned by formulating a screening rule. Thirdly, fine screening work is further carried out manually, a coarse screening result is corrected, and finally a bridge crack detection data set is obtained. According to the method, manpower and calculation time can be saved, and the detection cost for solving corresponding crack detection and other building problems is reduced.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a bridge crack detection method. Background technique [0002] As of the end of 2018, my country has built more than 90,000 long-span bridges, more than 5,000 super-long-span bridges, and more than 100 super-long-span bridges with a main span of more than 400 meters. Most of the bridges are located in the traffic arteries. Once a collapse accident occurs, it will not only cause huge economic losses, but also cause casualties of innocent people. Therefore, the safety of bridges is related to the national economy and the people's livelihood. In recent years, due to the rapid growth of traffic flow, it has caused great pressure on the operation safety of bridges. In addition, due to the long-term construction of bridges, poor design performance, and harsh natural environments, bridge collapses have occurred frequently in recent years, causing great losses. Exi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/13G06T7/136G06T7/62G06T7/80G06T5/40G06T5/20G06T5/00G06K9/62G06V10/762
CPCG06T7/0002G06T7/13G06T7/136G06T5/002G06T5/20G06T7/80G06T5/40G06T7/62G06T2207/30204G06T2207/20028G06F18/23
Inventor 张夷斋姬文鹏黄攀峰闫雨晨杨奇磊李鹏辉章永威
Owner NORTHWESTERN POLYTECHNICAL UNIV
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