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FDD: a deep learning–based steel defect detectors
Fityanul Akhyar
, Ying Liu
, Chao Yung Hsu
,
Timothy K. Shih
,
Chih Yang Lin
Department of Computer Science and Information Engineering
Department of Mechanical Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
55
Scopus citations
Overview
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Keyphrases
Automated Inspection Systems
33%
Bounding Box
33%
Cascade R-CNN
33%
Defect Detection
33%
Defect Detector
100%
Defect Inspection System
33%
Deformable Convolution
33%
Deformable RoI Pooling
33%
Detection Accuracy
33%
Detection Method
33%
Geometric Shape
33%
Guided Anchoring
33%
High Quality Products
33%
Industrial Manufacturing
66%
Inference Process
33%
Manufacturing Process
66%
Mean Average Precision
33%
Product Quality
33%
Random Scaling
33%
Region Proposal
33%
Scaling Technique
33%
Severstal
33%
Steel Defects
100%
Steel Surface Defect Detection
33%
Surface Defect Detection
33%
Surface Defects
33%
Training Process
33%
Computer Science
Baseline Architecture
50%
Convolutional Neural Network
50%
Deformable Convolution
50%
Detection Accuracy
50%
Detection Method
50%
Inference Process
50%
Inspection System
100%
Mean Average Precision
50%
Product Quality
100%
Scaling Technique
50%
Training Process
50%
Engineering
Bounding Box
33%
Convolutional Neural Network
33%
Defect Detection
66%
Geometric Shape
33%
High Quality Product
33%
Inference Process
33%
Input Image
33%
Manufacturing Process
66%
Product Quality
33%
Steel Surface
33%
Surface Defect
100%
Material Science
Surface Defect
100%