Remote system and detection method for planar material detection based on cloud big data

Abstract

The invention relates to a cloud big data-based planar material detection remote system and a cloud big data-based planar material detection method. On the basis of an existing linear array scanning scheme commonly used for planar material detection, a machine learning function (a support vector machine (SVM)) is added, difficult defect pictures acquired by all distributed client terminals are searched by constructing a defect big data center of a cloud end, a detection operator subjected to machine learning optimization is periodically updated to each distributed client terminal by applying a neural network screening system and necessary manual intervention so that the system can continuously accumulate characteristics of various kinds of defects and automatically learns and adapts to continuously improve the detection accuracy. Meanwhile, a light path is regulated by using parts such as an image quality evaluation function remote control camera cloud deck and a variable optical axis lens, and thus the stability of the system is ensured.

Description

technical field [0001] The invention relates to a remote system and a detection method for planar material detection based on cloud big data. Background technique [0002] Due to various reasons in the production process of planar materials, various defects will inevitably occur on the surface. It is very important to find such defects in time. The sooner the problems are found, the faster they can be eliminated, thereby avoiding larger problems. Quality issues. Due to the wide variety of planar materials, different materials have different materials, textures, colors, optical properties, etc., and the types of defects produced will vary widely due to different production equipment, environments and processes. Faced with such complex and diverse detection objects, the existing detection equipment on the market generally can only rely on a limited number of defect samples provided by the customer factory for algorithm construction and debugging. The coverage of defect types ...

AI Technical Summary

Problems solved by technology

Faced with such complex and diverse detection objects, the existing detection equipment on the market generally can only rely on a limited number of defect samples provided by the customer factory for algorithm construction and debugging. The coverage of defect types is limited and cannot cope with actual work. The special defect types that may appear in the environment, and quickly adjust the algorithm to apply, there are common problems such as difficult debugging, poor stability, and low detection accuracy

At the same time, since customers are widely scattered all over the country, it poses a severe challenge to the after-sales service of system solution providers

If an emergency occurs and cannot be responded to in time, it will seriously affect the customer's production plan and damage the customer's interests

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