基于BP神经网络的线Mura缺陷识别与定位研究

Mura detection and positioning in picture based on BP neural network

各类显示屏中Mura缺陷的自动识别和定位对提高显示屏幕的产品品质具有重要作用,是当前迫切需要发展的重要技术之一。针对当前手机屏幕Mura缺陷对比度低、缺乏明显边缘等特点,文中提出一种基于图像灰度曲线的Mura缺陷检测方法及其改进方法。改进方法基于均值滤波平滑和降采样原理,通过研究采样线上灰度曲线的波峰与波谷信息,利用BP神经网络构建线Mura缺陷的自动检测和定位算法。结合真实手机屏幕图像验证结果表明,与现有多种Mura缺陷检测方法相比,本文的改进方法能更准确快速地识别手机屏幕中的线Mura缺陷,识别准确率达到98.33%,检测过程无需调节参数,实现了线Mura缺陷的自动检测和定位。

Automatic identification and location of Mura defect in various screens plays an important role in improving the quality of screens.It is one of the most important technologies that need to be developed urgently.Aiming at the features of low contrast and lack of obvious edge of Mura defect,this paper proposes a method of Mura detection based on image gray curve and its improved method.This improved method is based on the principle of mean filter to smooth the picture and down-sampling.By studying the information about peak and trough of the gray curve on sampling lines,the BP neural network is used to construct an automatic detection and location algorithm for line Mura.The experimental results show that,compared with the existing Mura detection methods,the improved method in this paper can distinguish line Mura defect on the mobile phone screen more accurately and quickly.The accuracy rate is 98.33%,and no parameter needs to be adjusted during the detection process,realizing automatic detection,and positioning of line Mura.