Part defect detection method and device and electronic equipment

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. [...

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 the prior art. Labeling information at the pixel level, therefore, the required labor cost is relatively high, and it is impossible to deal with the correlation between the defects of each plane of the component

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