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基于区域神经网络的TFT-LCD电路缺陷检测方法
Authors
何俊杰
刘畅
肖可
陈松岩
Publication date
15 July 2018
Publisher
Abstract
对薄膜晶体管液晶显示器(TFT-LCD)边框电路中细微、复杂的缺陷进行检测,一直是自动光学检测(AOI)的一个难点。本文提出基于改进的快速区域神经网络(Faster R-CNN)算法对TFT-LCD边框电路的缺陷进行检测。首先在共享卷积层进行特征提取,然后通过多层的区域提议网络结构生成精确候选区域,根据候选区域的特征和目标分类实现对缺陷的识别和定位。同时设计多种有效的网络结构并详细分析网络层深度及卷积核大小对检测效果的影响,最后进行不同算法的比较。在实际构建的数据集上实验,结果表明本文方法具有良好的检测效果,对6种类别的液晶屏边框电路缺陷识别定位达到平均每张0.12 s的检测速度和94.6%的准确率。福建省高校产学合作项目(2016H6026
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Last time updated on 10/06/2020