Plastic part defect detection method based on multiple threshold intervals

Abstract

The invention provides a plastic part defect detection method based on multiple threshold intervals. The method comprises the following steps: S1, inputting a grayed image of a surface picture of a detected part; S2, obtaining a defect eigenvalue vector of the grayed image of the detected part and a defect threshold interval in multiple threshold intervals of the defect; and S3, calculating a defect type corresponding to the defect eigenvalue vector by using a least distance classifier according to the defect threshold interval. Through the plastic part defect detection method, multiple threshold intervals are set, and the least distance classifier is used; the automation detection of the surface defects of the plastic parts is achieved; the detection efficiency of the surface defects of the plastic parts is improved; the manpower, the material resources and the financial resources are reduced.

Description

technical field [0001] The invention relates to the technical field of component defect detection, in particular to a plastic component defect detection method based on multiple threshold intervals. Background technique [0002] In modern society, plastic parts are ubiquitous in life, and have been widely used in various industries such as electronics, chemical industry, and aerospace. Since the contour, shape, size and surface cleanliness of each part in plastic parts must be consistent with the accuracy of the original design in order to meet the production needs, therefore, in the rapidly developing industrial environment, detecting part defects is an indispensable link in the processing industry one. At present, most factories mainly rely on artificial naked eyes to detect surface defects of plastic parts. , and can only rely on naked eye positioning for reprocessing, which is likely to cause secondary processing defects, and if the workpiece is directly discarded, it ...

AI Technical Summary

Problems solved by technology

At present, most factories mainly rely on artificial naked eyes to detect surface defects of plastic parts. , and can only rely on naked eye positioning for reprocessing, which can easily cause secondary processing defects. If the workpiece is discarded directly, it will cause waste of resources

Therefore, manual detection of component defects not only consumes a lot of human resources, but also has low detection efficiency and poor effect

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