基于MapReduce的液晶屏缺陷检测方法

MapReduce approach for defect inspection of TFT-LCD

5^(th)液晶屏在生产过程中会产生多种类型的缺陷,通过单一节点进行缺陷检测存在存储资源和计算时间的瓶颈。利用Hadoop集群的分布式计算、存储能力处理海量的高分辨率液晶屏图像是一个新的思路。针对高分辨液晶屏图像缺陷局部性特点,设计基于MapReduce的分布式缺陷检测方法,对高分辨率图像分块,并行完成每块图像的缺陷检测,再将检测结果归并,从而解决高分辨率图像缺陷检测效率低下问题。通过运行在Hadoop平台上的实验表明,该方法在完成缺陷检测的同时具有良好的效率提升。

Various types of defects would come into being in the process of producing 6th TFT-LCD. There are bottlenecks of storing resources and calculating time by inspecting defects with one single machine. It is a new way to deal with massive high-resolution LCD images by Hadoop clusters, which has the advantage in computing and storage capacity. Since the defect is a local feature of high-resolution LCD image, a distributed method of defect inspection based on MapReduce framework is proposed in this paper. To solve the problem of low efficiency of high-resolution image in defect inspection,the approach can be simply described as follows. First, the high-resolution image is split into multiple small splits, which are parallel inspected in the following step. In the final step, the intermediate results are aggregated to obtain the final result. The experimental results show that this approach can inspect defects simultaneously on Hadoop cluster with a good speed up rate.