Deep hashing network for material defect image classification

Deep hashing network for material defect image classification

Common non‐destructive material testing technology has some well‐known problems such as slow detection, low detection accuracy, and low level of information obtained. To solve these problems, this study applied recent advances in convolution neural networks to propose an effective deep learning netw...

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Bibliographic Details
Main Authors: Kai Yang, Zhiyi Sun, Anhong Wang, Ruizhen Liu, Qianlai Sun, Yin Wang
Format: Article
Language:English
Published: Wiley 2018-12-01
Series:IET Computer Vision
Subjects:
intelligent detection technology
multidimensional visual feature vector
deep hashing network
multiple convolution-pooling layers
image representations
compact binary hash codes
Online Access:https://doi.org/10.1049/iet-cvi.2018.5286

Internet

https://doi.org/10.1049/iet-cvi.2018.5286

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