Metal paint spraying surface defect detection method

Abstract

The invention relates to a metal paint spraying surface defect detection method. The method comprises the steps of acquiring a metal paint spraying surface image data set containing defect images anddefect-free images; carrying out primary selection on the metal paint spraying surface image data set by adopting a binary classification mode to obtain a positive sample with a label, a positive sample without a label and a negative sample without a label; acquiring an image containing unknown types of defects on the surface of metal paint spraying, and performing training test on a deep learningneural network in combination with a sample obtained by binary classification primary selection to obtain a defect detection model; and inputting the actual metal paint spraying surface image into the defect detection model, and outputting a defect detection result of the actual metal paint spraying surface image. Compared with the prior art, the method has the advantages that early-stage samplescreening and labeling can be accurately and quickly carried out by combining blob block detection and the deep learning neural network, and meanwhile, the neural network is trained by utilizing the defect images of unknown types, so that the metal paint spraying surface defects can be quickly, accurately and comprehensively detected by the method.

Description

technical field [0001] The invention relates to the technical field of metal spray paint surface detection, in particular to a metal spray paint surface defect detection method. Background technique [0002] In order to ensure the appearance quality of metal spray paint products, it is necessary to carry out defect detection on the metal spray paint surface to screen out products with paint spray defects on the surface. At present, the commonly used detection methods are mainly divided into two methods: manual detection and machine image recognition. Among them, manual detection has the problems of low detection efficiency and low detection accuracy; machine image recognition uses deep learning to analyze the metal sprayed surface. Automatic defect detection, this method needs to use positive and negative samples for training and testing, and positive and negative samples are usually separated by manual screening and labeling, which undoubtedly increases the workload in the ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a metal spray paint surface defect detection method in order to overcome the defects in the above-mentioned prior art, aiming at the inaccuracy of the

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