Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding

Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding

Convolutional Neural Network (CNN) has become an increasingly important research field in machine learning and computer vision. Deep image features can be learned and subsequently used for detection, classification and retrieval tasks in an end-to-end model. In this paper, a supervised feature embed...

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Bibliographic Details
Main Authors: Yan Zhang, Xuehong Cui, Yun Liu, Bin Yu
Format: Article
Language:English
Published: Atlantis Press 2018-01-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Deep learning
Defect classification
CNN
AlexNet
Tire defects
Online Access:https://www.atlantis-press.com/article/25895960/view

Internet

https://www.atlantis-press.com/article/25895960/view

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