A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection-Reference-Cited by-同舟云学术

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A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection

Published:2024-01 Issue: Volume:59 Page:102280
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Wang Guan-Qiang,Zhang Chi-Zhou,Chen Ming-Song,Lin Y.C,Tan Xian-Hua,Kang Yu-Xin,Wang Qiu,Zeng Wei-Dong,Zhao Wei-Wei

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference61 articles.

1. Automated visual defect classification for flat steel surface: a survey;Luo;IEEE t. Ind. Inform.,2020

2. Efficient surface defect detection using self-supervised learning strategy and segmentation network;Xu;Adv. Eng. Inform.,2022

3. A lightweight detector based on attention mechanism for aluminum strip surface defect detection;Ma;Comput. Ind.,2022

4. TAF2-Net: triple-branch attentive feature fusion network for ultrasonic flaw detection;Li;IEEE t. Instrum. Meas.,2022

5. Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters;Nacereddine;Comput. Ind.,2019

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1. LSKA-YOLOv8: A lightweight steel surface defect detection algorithm based on YOLOv8 improvement;Alexandria Engineering Journal;2024-12

2. Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search;Advanced Engineering Informatics;2024-10

3. Revisiting the application of twin connected parallel networks and regression loss functions in industrial defect detection;Advanced Engineering Informatics;2024-10

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5. Few-shot unseen defect segmentation for polycrystalline silicon panels with an interpretable dual subspace attention variational learning framework;Advanced Engineering Informatics;2024-10

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