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A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks

Keming Mao, Duo Lu, Dazhi E, Zhenhua Tan
2018 Sensors  
This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts.  ...  Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s18061871 pmid:29880774 pmcid:PMC6022075 fatcat:dt2xii23mjddzayrmccaegg5n4

Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model

He Yin, Keming Mao, Jianzhe Zhao, Huidong Chang, Dazhi E, Zhenhua Tan
2019 Applied Sciences  
This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models.  ...  Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight CNNs models were selected.  ...  Our previous work performs a case study on heated metal attribute recognition based on CNNs [25] . We analyzed and selected seven heated metal attributes.  ... 
doi:10.3390/app9091955 fatcat:vi5srtm7grfkde4xtrfzv2ikne

2019 Index IEEE Transactions on Semiconductor Manufacturing Vol. 32

2019 IEEE transactions on semiconductor manufacturing  
., +, TSM May 2019 190-198 Image retrieval Wafer Defect Pattern Recognition and Analysis Based on Convolutional Neural Network.  ...  ., +, TSM Aug. 2019 302-309 Wafer Defect Pattern Recognition and Analysis Based on Convolutional Neural Network.  ...  Profitability A Productivity-Oriented Wafer Map Optimization Using Yield Model Based on Machine Learning.  ... 
doi:10.1109/tsm.2019.2958442 fatcat:e276xgw6gbbdlc4fy2ldbrd4py

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

Jing Yang, Shaobo Li, Zheng Wang, Hao Dong, Jun Wang, Shihao Tang
2020 Materials  
Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method  ...  The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection.  ...  The deep residual network adds a residual module on the basis of the convolutional neural network.  ... 
doi:10.3390/ma13245755 pmid:33339413 pmcid:PMC7766692 fatcat:egyrdqjmqvebvoeaqf5yyi3cxm

Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images

Xin Li, Yonggang Li, Renchao Wu, Can Zhou, Hongqiu Zhu
2021 Frontiers in Neurorobotics  
This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN).  ...  Raw infrared images without fault are used as backgrounds. By simulating the other two key variables on the background, different classes of objects are synthesized.  ...  Robust license plate recognition using neural networks trained on synthetic images.  ... 
doi:10.3389/fnbot.2021.751037 pmid:34899228 pmcid:PMC8662818 fatcat:fqzpi7rmwzgyjbg5uxbdjqboxi

Intelligent Localization of Transformer Internal Degradations Combining Deep Convolutional Neural Networks and Image Segmentation

Jiajun Duan, Yigang He, Bolun Du, Ruaa M. Rashad Ghandour, Wenjie Wu, Hui Zhang
2019 IEEE Access  
In this study, an efficient fault localization method for transformer internal thermal faults was proposed by introducing different deep convolutional neural networks (CNNs) and image segmentation.  ...  The transformer is one of the critical power devices, its intelligent monitoring and fault positioning require indepth studies.  ...  Fault diagnosis accuracies are limited by the lack of fault samples, which is obvious when using deep Convolutional Neural Networks (CNNs).  ... 
doi:10.1109/access.2019.2916461 fatcat:6blrerabjfguji5g3s6lcajjue

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

Zhuangzhuang Hao, Zhiyang Li, Fuji Ren, Shuaishuai Lv, Hongjun Ni
2022 Metals  
In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism.  ...  By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm.  ...  [12] proposed an end-toend recognition system based on symmetric surround saliency map and deep convolutional neural network (CNN).  ... 
doi:10.3390/met12020311 doaj:971767c44879452fa5323200426e74d3 fatcat:ejom3typgngntlgtwxavptcmau

Deep Learning-Guided Surface Characterization for Autonomous Hydrogen Lithography [article]

Mohammad Rashidi, Jeremiah Croshaw, Kieran Mastel, Marcus Tamura, Hedieh Hosseinzadeh, Robert A. Wolkow
2019 arXiv   pre-print
We trained a convolutional neural network to locate and differentiate between surface features of the technologically relevant hydrogen-terminated silicon surface imaged using a scanning tunneling microscope  ...  Here we present an automation method for the identification of defects prior to atomic fabrication via hydrogen lithography using deep learning.  ...  Subsequent model retraining was done using a learning rate of 0.001 which very slightly improved network performance in this case.  ... 
arXiv:1902.08818v2 fatcat:4emwlxafz5gntfhvtpundqcjya

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary  ...  ., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang,  ...  Radar-Based Human Gait Recognition Using Dual-Channel Deep Convolutional Neural Network.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Defect detection of seals in multilayer aseptic packages using deep learning

2019 Turkish Journal of Electrical Engineering and Computer Sciences  
Therefore, this study aims to perform a leak test in aseptic package seals by a system that makes decisions using independent deep learning methods.  ...  The proposed Faster R-CNN and the Updated Faster R-CNN deep learning models were subjected to training and testing with a total of 400 images taken from a real production environment, resulting in a correct  ...  Acknowledgment This work was supported by the Research Fund of the TÜBİTAK-TEYDEB, Project Number 2150314.  ... 
doi:10.3906/elk-1903-112 fatcat:aluqmsovajbzpoqgcesfclxwyy

Lasers that learn: The interface of laser machining and machine learning

Benjamin Mills, James A. Grant‐Jacob
2021 IET Optoelectronics  
However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those  ...  This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  ...  Data supporting this study are openly available from the University of Southampton repository at https://doi.org/10.5258/ SOTON/D1710. ORCID Benjamin Mills https://orcid.org/0000-0002-1784-1012  ... 
doi:10.1049/ote2.12039 fatcat:loc2t2l6end63k7sbpeubvys5u

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Andrej Thurzo, Helena Svobodová Kosnáčová, Veronika Kurilová, Silvester Kosmeľ, Radoslav Beňuš, Norbert Moravanský, Peter Kováč, Kristína Mikuš Kuracinová, Michal Palkovič, Ivan Varga
2021 Healthcare  
Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks.  ...  In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/healthcare9111545 pmid:34828590 pmcid:PMC8619074 fatcat:wnexmx27gjfbjlri6tmy5rwkv4

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Case Study on Zero-Shot ASL Recognition DAY 1 -Jan 12, 2021 Sharma, Renu; Ross, Arun 1677 Viability of Optical Coherence Tomography for Iris Presentation Attack Detection DAY 1 -Jan 12, 2021  ...  ; Skocaj, Danijel 1532 End-To-End Training of a Two-Stage Neural Network for Defect Detection GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks Deep  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Extraction and Analysis of Blue Steel Roofs Information Based on CNN Using Gaofen-2 Imageries

Meiwei Sun, Yingbin Deng, Miao Li, Hao Jiang, Haoling Huang, Wenyue Liao, Yangxiaoyue Liu, Ji Yang, Yong Li
2020 Sensors  
Here, the DeeplabV3+ semantic segmentation neural network based on GaoFen-2 images was used to analyze the quantity and spatial distribution of blue steel roofs in the Nanhai district, Foshan (including  ...  heat islands.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20164655 pmid:32824822 fatcat:rpuqp5kupngaxndqmrt3svwuli

Overview of Memristor-Based Neural Network Design and Applications

Longcheng Ye, Zhixuan Gao, Jinke Fu, Wang Ren, Cihui Yang, Jing Wen, Xiang Wan, Qingying Ren, Shipu Gu, Xiaoyan Liu, Xiaojuan Lian, Lei Wang
2022 Frontiers in Physics  
deep neutral networks (DNNs) and spike neural networks (SNNs).  ...  One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain.  ...  To address this problem, a deep-learning algorithm was developed for the perceptron based on a deep neural network (DNN).  ... 
doi:10.3389/fphy.2022.839243 fatcat:vd3tpopvrjd4la6eivu36b73aq
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