Automated defect inspection of LED chip using deep convolutional neural network
Automated defect inspection of LED chip using deep convolutional neural network
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DOI：
10.1007/s10845-018-1415-x
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发表时间：
2019-08-01
影响因子：
8.3
通讯作者：
Niu, Shuanglong
中科院分区：
文献类型：
--
作者：
Lin, Hui;Li, Bin;Niu, Shuanglong
缺陷检测是控制LED芯片质量的重要环节。一方面，传统方法耗时长，对模型依赖严重，需要丰富的操作经验；另一方面，传统网络无法实现缺陷定位。为了解决这些问题，我们实现了卷积神经网络（CNN）在LED芯片缺陷检测中的应用。在CNN中，提出了一种类激活映射技术，在不使用区域级人工注释的情况下对缺陷区域进行局部化。进一步，收集LED芯片数据集用于训练CNN。值得强调的是，芯片缺陷分类和定位任务是在单个CNN中完成的，非常快速和方便。本文提出的基于CNN的缺陷检测器LEDNet在检测LED芯片缺陷（线缺陷和划痕）方面取得了令人印象深刻的高性能，其不准确性为5.04%，并且精确定位了缺陷区域。
Defect inspection is a vital part of the production process to control the quality of LED chip. On the one hand, traditional methods are time-consuming, which rely on models badly and require rich operation experience. On the other hand, defect localization cannot be achieved by using traditional networks. To solve these problems, we achieve the application of convolutional neural network (CNN) for LED chip defect inspection. Built in the CNN, a class activation mapping technique is proposed to localize defect regions without using region-level human annotations. Further, LED chip datasets are collected for training the CNN. It is worth to emphasize that the chip defect classification and localization tasks are completed in a single CNN which is very fast and convenient. The proposed CNN based defect inspector named LEDNet achieves impressively high performance on the inspection of LED chip defects (line blemishes and scratch marks) with an inaccuracy of 5.04%, localizing exact defect regions as well.