基于图像预处理和轻量级卷积神经网络的铁路紧固件缺陷分类

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

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DEFECT CLASSIFICATION OF RAILWAY FASTENERS USING IMAGE PREPROCESSING AND A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK
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来源期刊
Turkish Journal of Electrical Engineering and Computer Sciences
Turkish Journal of Electrical Engineering and Computer Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
2.90
自引率
9.10%
发文量
95
审稿时长
6.9 months
期刊介绍: The Turkish Journal of Electrical Engineering & Computer Sciences is published electronically 6 times a year by the Scientific and Technological Research Council of Turkey (TÜBİTAK) Accepts English-language manuscripts in the areas of power and energy, environmental sustainability and energy efficiency, electronics, industry applications, control systems, information and systems, applied electromagnetics, communications, signal and image processing, tomographic image reconstruction, face recognition, biometrics, speech processing, video processing and analysis, object recognition, classification, feature extraction, parallel and distributed computing, cognitive systems, interaction, robotics, digital libraries and content, personalized healthcare, ICT for mobility, sensors, and artificial intelligence. Contribution is open to researchers of all nationalities.
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