Product defect detection method, apparatus and system, server and storage medium

Abstract

The invention provides a product defect detection method, apparatus and system, a server and a computer readable storage medium. The product defect detection method comprises the steps of obtaining image data of a product; and converting the image data into a classification request, determining an executive server according to a deployment condition of classification prediction models in multipleservers, and sending the classification request to the executive server, thereby giving out a defect type prediction result through the classification prediction model in the executive server. According to the product defect detection method, apparatus and system provided by the invention, the model can be iterated along with business development, thereby enabling the model to meet latest demandsof a production environment; an industrial production line is remarkably improved in the aspects of classification precision, expandability, standardization and the like; and parallel processing further improves the efficiency.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a product defect detection method, device, system, server and computer-readable storage medium. Background technique [0002] At present, the quality inspection link in many production industries mainly uses visual methods to detect defects on product surface images. Taking the paper industry as an example, the detection of paper quality is mainly based on the pictures on the surface of the paper. In the production line of the paper industry, the quality inspection is mostly manual inspection or semi-automatic optical instrument-assisted quality inspection, which is not only inefficient, but also prone to misjudgment; in addition, the industrial data generated in this way is not easy to store, manage and secondary mining Reuse. In the case of manual inspections, business experts are required to conduct inspections on the production site, and manually record defects...

AI Technical Summary

Problems solved by technology

This method is not only inefficient, it is easy to miss and misjudgment, and it is difficult to mine the data for secondary use, and the production environment is often harsh, which will adversely affect the health and safety of personnel.

The latter quality inspection method is mostly a quality inspection system based on traditional expert systems or feature engineering. The features and judgment rules are solidified into the machine based on experience, and it is difficult to iterate with the development of the business. As a result, with the development of the productio

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