TireNet: A high recall rate method for practical application of tire defect type classification-Reference-Cited by-同舟云学术

TireNet: A high recall rate method for practical application of tire defect type classification

Published:2021-12 Issue: Volume:125 Page:1-9
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Li Ying,Fan Binbin^ORCID,Zhang Weiping,Jiang Zhiqiang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference23 articles.

1. A dictionary-based method for tire defect detection;Xiang,2014

2. Automatic detection of defects in tire radiographic images;Zhang;IEEE Trans. Autom. Sci. Eng.,2017

3. Tire defect detection using image component decomposition;Guo;Res. J. Appl. Sci. Eng. Technol.,2012

4. Defect detection in tire x-ray images using weighted texture dissimilarity.;Guo;J. Sensors,2016

5. Detection of impurity and bubble defects in tire x-ray image based on improved extremum filter and locally adaptive-threshold binaryzation;Zheng,2018

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