AI vision-based lcd liquid crystal display screen defect detection system

The AI ​​vision-based LCD display defect detection system utilizes deep neural networks to decompose images and perform cross-layer consistency verification, solving the problem of balancing multiple types of defect detection in existing technologies, improving adaptability and robustness, and achieving efficient and accurate defect detection.

CN122415532APending Publication Date: 2026-07-17SHENZHEN XUANCAI SHIJIA TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN XUANCAI SHIJIA TECH CO LTD
Filing Date
2026-04-23
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies are difficult to handle multiple types of defect detection, lack adaptability to products of different specifications, have insufficient detection robustness, and are easily affected by industrial environmental noise.

Method used

An AI-based vision-based LCD display defect detection system is employed, comprising a system initialization and adaptive prior parameter generation module, an image acquisition module, a physical prior-based adaptive image decomposition module, a cross-layer residual consistency verification and defect enhancement module, and a defect location and result output module. The system decomposes the image into a periodic structure layer, a basic brightness layer, and an anomalous residual layer using a deep neural network, and enhances the defect signal while suppressing noise through cross-layer consistency verification.

Benefits of technology

It achieves effective decoupling of different types of defects, improves the adaptability and robustness of detection, reduces the calibration cost for new product models, and improves the accuracy and stability of detection.

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Abstract

本申请涉及机器视觉与人工智能技术领域,公开了基于AI视觉的LCD液晶显示屏缺陷检测系统,包括:系统初始化与自适应先验参数生成模块根据待测屏物理参数生成约束参数;基于物理先验的自适应图像分解模块结合所述约束参数,将采集的输入图像分解为周期性结构层、基础辉度层和异常残差层;跨层残差一致性校验与缺陷增强模块通过分析各层信号响应一致性增强真实缺陷,生成增强异常图;缺陷定位与结果输出模块处理所述增强异常图,精确定位缺陷并输出结构化检测结果。本发明采用将输入图像自适应地分解为具有明确物理意义的周期性结构层、基础辉度层与异常残差层的技术方案,达到了对不同物理成因的缺陷信号进行有效解耦的技术效果。
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