Thin film surface defect detection method, system and equipment based on deep learning

Abstract

The invention provides a film surface defect detection method, system and equipment based on deep learning. The film surface defect detection method based on deep learning comprises the steps: S1, conducting image acquisition on the front face of a detected material; S2, carrying out image acquisition on the reverse side of the detected material; S3, performing image preprocessing; S4, carrying out contour appearance defect detection; S5, judging a contour detection result; S6, performing fusion by using multiple channels to obtain a multi-channel fusion image; and S7, importing into a pre-trained deep learning model. The film surface defect detection system based on deep learning comprises an image acquisition module, an image preprocessing module, an image fusion module and an image detection module, thin film surface defect detection equipment based on deep learning comprises a memory and a processor. The method has the beneficial effects that the defects of the optical thin film can be effectively identified on the premise of low cost, the labor cost is reduced, the identification efficiency is improved, the deployment is simple, and the repeatability is higher.

Description

technical field [0001] The invention relates to the field of optical thin film detection, in particular to a method, system and equipment for detecting defects on the surface of thin films based on deep learning. Background technique [0002] Optical thin films are widely used in liquid crystal displays of mobile phones, computers, and televisions, glasses coatings, solar panels, and other fields. Optical films are composed of thin layered media through which light beams propagate, mainly including reflective films, anti-reflective films, filter films, optical protective films, polarizing films, etc., which have extremely high requirements for surface quality. In the assembly line production process, due to vibration, air flow and other reasons, various types of defects will occur on the surface of the optical film, such as scratches, indentations, corners, pits, black spots, black spots, white spots, wrinkles, bubbles and foreign matter, etc., leading to a decline in the u...

AI Technical Summary

Problems solved by technology

In the assembly line production process, due to vibration, air flow and other reasons, various types of defects will occur on the surface of the optical film, such as scratches, indentations, corners, pits, black spots, black spots, white spots, wrinkles, bubbles and foreign matter, etc., leading to a decline in the use effect of the product

[0003] In the field of traditional optical film production, due to the optical properties of optical films, it is difficult to image and identify various defects, which makes it difficult to realize automatic monitoring. Most optical films can only be detected by manual visual inspection, but this detection method On the one hand, it is easily affected by subjective judgments, resulting in unstable test results and poor reliability. On the other hand, it is labor-intensive and costly, making it difficult to meet the needs of mass production

However, those that can realize automatic detection can only detect a small number of types of defects, and cannot detect all defects at the same time. The deployment is complicated and the repeatability is poor.

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