特征提取
卷积神经网络
计算机科学
人工智能
瓦片
特征(语言学)
人工神经网络
保险丝(电气)
任务(项目管理)
光学(聚焦)
模式识别(心理学)
融合机制
计算机视觉
工程类
融合
艺术
语言学
哲学
物理
系统工程
光学
脂质双层融合
电气工程
视觉艺术
作者
Luofeng Xie,Xiao Xiang,Huining Xu,Ling Wang,Lijun Lin,Guofu Yin
标识
DOI:10.1109/tie.2020.2982115
摘要
Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this article, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module, and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.
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