Pipeline welding seam detection method

A welding seam detection and pipeline technology, which is applied in the fields of unstructured text data retrieval, instruments, image data processing, etc. Effect

Pending Publication Date: 2022-07-29
SHANGHAI ELECTRICGROUP CORP
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  • Claims
  • Application Information

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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a pipeline weld detection method in order to overcome the defect of low efficiency of pipeline weld detection in the prior art

Method used

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Embodiment Construction

[0037] The present invention is further described below by means of a preferred embodiment, but the present invention is not limited to the scope of the described embodiment.

[0038] This embodiment provides a pipeline weld detection method. refer to figure 1 , the pipeline weld detection method includes the following steps:

[0039] Step S1, acquiring a weld image.

[0040] Step S2, identifying the weld image to determine whether the weld corresponding to the weld image has defects.

[0041] Step S3, if there is a defect in the weld, determine the level corresponding to the defect.

[0042] Step S4, generating an alarm prompt according to the level corresponding to the defect.

[0043] In some optional embodiments, the pipeline weld detection method is implemented in the form of weld detection software and runs on a computer platform. The structural reference of this weld inspection software figure 2 As shown, it includes three modules: image acquisition, weld inspect...

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Abstract

The invention discloses a pipeline welding seam detection method. The method comprises the following steps: acquiring a welding seam image; identifying the welding seam image to determine whether the welding seam corresponding to the welding seam image has defects or not; if the weld joint has the defect, determining the level corresponding to the defect; and generating an alarm prompt according to the level. The pipeline welding seam detection efficiency is improved, digital management of films, real-time monitoring and abnormity warning of welding seam image defects, historical image sample management, image sample marking, manual rechecking, welding seam knowledge graph management, automatic defect grading, detection report summarization and export and the like are achieved, and a welding integrated detection system is formed. The deep learning technology and welding mechanism knowledge are combined, the existing detection efficiency and detection precision are improved, a foundation is laid for welding equipment operation and maintenance and welding quality online detection productization direction research and development, and meanwhile the method can be further applied to various industrial welding quality detection scenes.

Description

technical field [0001] The invention belongs to the technical field of pipeline weld detection, in particular to a pipeline weld detection method. Background technique [0002] At present, pipeline weld detection mainly starts from hardware and algorithm, and focuses on image acquisition and detection. For the integrated application of the detected images lacking image sample management, labeling, manual review, weld knowledge mechanism and other modules, it is impossible to achieve defect origin, knowledge map-assisted defect location, manual review, etc., still requires a lot of manual intervention, and cannot Fundamentally reduce the manual workload, and the efficiency is low. For the detection of the historical defect occurrence rate under the same or similar material specifications and production methods, it is impossible to warn in advance, thereby reducing the cost of the factory. SUMMARY OF THE INVENTION [0003] The technical problem to be solved by the present i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06V10/22G06V10/82G06N3/04G06F16/36G01N23/04
CPCG06T7/0004G06V10/22G06F16/367G06V10/82G01N23/04G06T2207/20084G06N3/045
Inventor 韩少恒叶松霖陈怡然董亚明
Owner SHANGHAI ELECTRICGROUP CORP
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