Part defect detection method and device and electronic equipment

A defect detection and parts technology, applied in computer parts, image data processing, instruments, etc., can solve the problems of inability to handle parts and high labor costs

Pending Publication Date: 2020-10-13
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem of whether there are defects or defects in parts products in the industrial quality inspection scene, a method for detecting parts defects based on deep learning is proposed in ...

Method used

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  • Part defect detection method and device and electronic equipment
  • Part defect detection method and device and electronic equipment
  • Part defect detection method and device and electronic equipment

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Embodiment Construction

[0032] Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0033] figure 1 A flow chart showing a component defect detection method according to an embodiment of the present application.

[0034] Such as figure 1 As shown, the component defect detection methods include:

[0035] Step S101: Obtain plane images of multiple planes of a component. Exemplarily, the component may be a component that requires defect detection i...

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Abstract

The invention discloses a part defect prediction method and device and electronic equipment, and relates to the fields of artificial intelligence, deep learning, cloud computing and computer vision, in particular to the aspect of industrial quality inspection. According to the specific implementation scheme, the method includes: obtaining plane images of multiple planes of the part; cutting the plane image according to a reference size to obtain a plurality of sub-images; inputting the plurality of sub-graphs into a pre-trained defect prediction model to obtain a defect prediction result of each sub-graph; and inputting the defect prediction result into a pre-trained defect identification model to obtain a defect identification result of each sub-graph, the defect identification result comprising a defect type and a defect grade. Through the above scheme, on one hand, accurate identification of real defects of the part can be realized, so that the accuracy of a defect detection resultis improved, and the probability of misjudgment on whether the part has defects or not is reduced; and on the other hand, the input data of the defect prediction model is convenient to process, and the labor cost can be reduced.

Description

technical field [0001] This application relates to the fields of artificial intelligence, deep learning, cloud computing and computer vision, especially the field of industrial quality inspection. Background technique [0002] With the development of artificial intelligence technology, computer vision is used in various scenarios, such as industrial quality inspection, power inspection, unmanned driving, smart retail, etc., all rely on artificial intelligence technology to complete specific tasks. At present, the combination of industrial production and deep learning is the general trend. On the one hand, it can ensure performance and stability, and on the other hand, it can greatly reduce labor costs. Generally speaking, the requirements of different industrial scenarios require customized design solutions, but the abstract representation of the requirements proposed for these scenarios can extract a set that can be extended to the same or similar industrial scenarios. [...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06T7/62G06K9/62
CPCG06T7/0008G06T7/10G06T7/62G06T2207/10004G06T2207/20081G06T2207/30164G06F18/24
Inventor 苑鹏程林书妃张滨韩树民徐英博冯原辛颖王晓迪刘静伟文石磊章宏武丁二锐
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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