IDD-Net: Industrial defect detection method based on Deep-Learning-Reference-Cited by-同舟云学术

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IDD-Net: Industrial defect detection method based on Deep-Learning

Published:2023-08 Issue: Volume:123 Page:106390
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Zhang Zekai^ORCID,Zhou Mingle^ORCID,Wan Honglin^ORCID,Li Min^ORCID,Li Gang^ORCID,Han Delong^ORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference48 articles.

1. VF-SIFT: Very fast SIFT feature matching;Alhwarin,2010

2. Mining big data streams using business analytics tools: a bird’s eye view on MOA and SAMOA;Arunkumar;Int. J. Bus. Intell. Data Min.,2020

3. Aluminum defect dataset;Baidu,2021

4. Triplet-graph reasoning network for few-shot metal generic surface defect segmentation;Bao;IEEE Trans. Instrum. Meas.,2021

5. Surface defect detection methods for industrial products: A review;Chen;Appl. Sci.-Basel,2021

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1. LAD-Net: A lightweight welding defect surface non-destructive detection algorithm based on the attention mechanism;Computers in Industry;2024-10

2. EAFNet: Extraction-amplification-fusion network for tiny cracks detection;Engineering Applications of Artificial Intelligence;2024-08

3. Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network;Processes;2024-06-05

4. Dual-branch information extraction and local attention anchor-free network for defect detection;Scientific Reports;2024-05-13

5. DC-YOLO: A dual channel YOLO for small object defect detection of circuit boards;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19

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